AP_results
- Set up environment
- 0) Manipulation checks
- 1) Stress effects on memory
- 2) Trial-wise analyses
- Localizer classification:
- Load in data
- Are hipp quintiles matched across groups?
- Are VTC quintiles matched across groups?
- VTC Mediation: Indirect effect of hipp -> VTC -> pcorr
- Plots:
- 2b) Signal variation for HC associative hits
- Subj level effects
- 2b (i): Does hipp still track VTC for HC recollection?
- 2b (ii): Is frontoparietal activity modulated by RT/difficulty of HC decisions?
- 2b (ii): Excluding 2 subjs w/poor vtc localizer (trial-wise criteria from above)
- 2b (iii): Explore: Is hipp/vtc greater for faster high confidence memories? Or for longer RT (potentially retrieval of more things to guide associate recall) memories?
- 2b (iv): What about across all accurate (high AND low confidence) associative judgments (i.e., like Alan’s study)
- 3) Effects of group/encoding strength on VTC reinstatement
- Across old cues: pCorr ~ VTC reinstatement
- HC assoc hits: VTC reinstatement ~ group
- HC assoc hits: VTC reinstatement ~ group * encoding strength
- HC assoc hits: VTC reinstatement ~ group * run type (safe, threat)
- HC assoc hits: scene vs. face/object evidence:
- HC assoc hits: PPA ~ group
- Visualize timecourse of reinstatement
- 3b) Effects of group/encoding strength on inferior parietal reinstatement
- 4) Hipp peak from whole-brain analysis
- 5) Predictors of HC accuracy
- Calculate P(accurate|respond “high conf source” to old item)
- Is group -> source accuracy mediated by hippocampal activity for SHs (>CRs)?
- Is hipp effect on source accuracy mediated by VTC reinstatement?
- Is hipp effect on source accuracy mediated by infparietal reinstatement?
- 5b) Predictors of LC accuracy
- 6) Effects of stress on frontoparietal networks (and interactions by run type)
- 7) Variability in neural patterns during retrieval
- Supplemental stats
- S1) Encoding performance
- S2) Compare item vs. assoc dprime (Wixted approach)
- S3) Associative accuracy (behavioral inclusion criteria) by encoding strength and run type (safe, threat)
- S4) Localizer behavioral performance
- S5) Motion
- S6) Other ROI Analyses
- S7) Reinstatement
- S8) Inferior parietal reinstatement
- Load in data
- Other analyses
Set up environment
source('~/Experiments/AP/stats/init_renv.R')## Loading required package: Matrix##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
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## step##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union##
## Attaching package: 'tidyr'## The following object is masked from 'package:Matrix':
##
## expand##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## Visit http://strengejacke.de/sjPlot for package-vignettes.##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## select# plotting stuff
theme_set(theme_classic(base_size = 22))
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(RColorBrewer)
library(boot)##
## Attaching package: 'boot'## The following object is masked from 'package:car':
##
## logitzscore <- function(vec){(vec-mean(vec))/sd(vec)}
# run trial-wise mediation? (takes a long time - only do first time)
run_mediation = FALSE0) Manipulation checks
00) Compare CTRU vs. Salimetrics assay on independent set of 49 samples (Salimetrics ran these using data from Gotlib lab)
dcort_salcompare = read.csv('/Volumes/group/awagner/sgagnon/AP/data/cortisol/salimetrics_comparison_n49samples.csv')
dim(dcort_salcompare)## [1] 49 2# correlation between assays
res_cor = cor.test(dcort_salcompare$lnSal_CORt_Mean,
y = dcort_salcompare$lnSAL_IBL_mean)
res_cor##
## Pearson's product-moment correlation
##
## data: dcort_salcompare$lnSal_CORt_Mean and dcort_salcompare$lnSAL_IBL_mean
## t = 28.624, df = 47, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9514994 0.9844757
## sample estimates:
## cor
## 0.9724964cat('R^2: ', (res_cor$estimate)^2)## R^2: 0.9457493# mean of assays
dcort_salcompare %>% summarise_all(funs(mean, sd))## lnSal_CORt_Mean_mean lnSAL_IBL_mean_mean lnSal_CORt_Mean_sd
## 1 -2.639796 -2.540204 1.043768
## lnSAL_IBL_mean_sd
## 1 1.072183# compare means
bartlett.test(value ~ key, dcort_salcompare %>% gather())##
## Bartlett test of homogeneity of variances
##
## data: value by key
## Bartlett's K-squared = 0.034268, df = 1, p-value = 0.8531t.test(dcort_salcompare$lnSal_CORt_Mean,
dcort_salcompare$lnSAL_IBL_mean,
var.equal=TRUE, paired = TRUE)##
## Paired t-test
##
## data: dcort_salcompare$lnSal_CORt_Mean and dcort_salcompare$lnSAL_IBL_mean
## t = -2.7916, df = 48, p-value = 0.007507
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.17132349 -0.02786019
## sample estimates:
## mean of the differences
## -0.09959184t.test(value ~ key, dcort_salcompare %>% gather(),
var.equal=TRUE, paired = TRUE)##
## Paired t-test
##
## data: value by key
## t = -2.7916, df = 48, p-value = 0.007507
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.17132349 -0.02786019
## sample estimates:
## mean of the differences
## -0.099591840a) Cortisol
dcort = read.csv('/Volumes/group/awagner/sgagnon/AP/data/cortisol/combined_cort_results.csv')
head(dcort)## X Sample assay cv mean subid group study log.ug.dL. day
## 1 0 S1 IBL 6.3 0.2327 ap100 control fmri -1.624525 Day1
## 2 1 S2 IBL 4.0 0.1451 ap100 control fmri -2.068512 Day1
## 3 2 T1 IBL 1.9 0.1218 ap100 control fmri -2.233052 Day2
## 4 3 T2 IBL 10.8 0.0632 ap100 control fmri -2.849764 Day2
## 5 4 T3 IBL 5.5 0.1655 ap100 control fmri -1.944857 Day2
## 6 5 S1 IBL 3.3 0.0972 ap101 control fmri -2.445125 Day1dcort %>% filter(log.ug.dL. > 0) # all neg## [1] X Sample assay cv mean subid
## [7] group study log.ug.dL. day
## <0 rows> (or 0-length row.names)# calculate %change relative to baselines
dcort_change = dcort %>%
dplyr::select(subid, group, Sample, log.ug.dL., assay) %>%
spread(key = Sample, value=log.ug.dL.) %>%
mutate(day1_change = (S2 - S1)/abs(S1) * 100,
day2_change1 = (T2 - T1)/abs(T1) * 100,
day2_change2 = (T3 - T1)/abs(T1) * 100,
day2_avgchange = (day2_change1 + day2_change2)/2)
dcort_change## subid group assay S1 S2 T1 T2
## 1 ap100 control IBL -1.6245249 -2.0685122 -2.2330524 -2.8497639
## 2 ap101 control IBL -2.4451255 -2.5756318 -2.0101161 -2.3110621
## 3 ap102 control IBL -2.2454824 -2.3220268 -2.2246012 -2.2846048
## 4 ap103 control IBL -0.7916171 -0.8377518 -1.1613053 -0.8806980
## 5 ap104 control IBL -1.8622621 -2.3610006 -2.6875986 -2.6305379
## 6 ap105 control IBL -1.7432022 -2.2952634 -1.8437190 -1.9262983
## 7 ap106 control IBL -1.8669555 -1.5814941 -1.2402958 -1.6229105
## 8 ap107 control IBL -1.2459496 -1.7010788 -1.6628639 -2.0217552
## 9 ap108 control IBL -2.3495746 -2.9838212 -1.7028334 -2.1328566
## 10 ap109 control IBL -1.0479879 -1.7844245 -0.9211022 -2.0242241
## 11 ap110 control IBL -1.4195487 -1.7969580 -1.5016348 -2.2927930
## 12 ap111 control IBL -1.9037394 -2.2764873 -1.2870716 -1.9831114
## 13 ap113 control IBL -1.3739025 -1.7519507 -2.3161068 -1.7887441
## 14 ap114 control IBL -1.8295453 -1.8945422 -2.4174905 -2.5195601
## 15 ap115 control IBL -1.8140984 -2.0322928 -1.3885587 -1.9914300
## 16 ap116 control Salimetrics -1.1647521 -1.3356012 -1.3704210 -1.0671136
## 17 ap117 control Salimetrics -1.8971200 -1.5186835 -1.4146938 -1.7429693
## 18 ap118 control Salimetrics -0.7916171 -1.4481698 -1.4229583 -1.9519282
## 19 ap119 control Salimetrics -2.3968958 -2.6036902 -2.2633644 -2.8824036
## 20 ap120 control Salimetrics -2.0874737 -2.1715568 -1.4961092 -1.5847453
## 21 ap121 control Salimetrics -1.7897615 -1.7092582 -2.7030627 -1.9241487
## 22 ap122 control Salimetrics -2.0249534 -2.7181005 -1.7544637 -2.0249534
## 23 ap150 stress IBL -1.0075262 -1.5333556 -1.3455587 -1.1150783
## 24 ap151 stress IBL -2.2034257 -1.6387707 -2.3495746 -1.9687286
## 25 ap152 stress IBL -1.4858183 -1.7993876 -2.9676652 -3.0467773
## 26 ap153 stress IBL -2.2318955 -2.1718076 -1.5159207 -2.0001241
## 27 ap154 stress IBL -1.9488413 -2.0562833 -2.1307777 -2.6376322
## 28 ap155 stress IBL -1.7054713 -1.7955033 -1.9935212 -1.6653919
## 29 ap156 stress IBL -1.5256880 -1.9174303 -1.2170978 -2.0354147
## 30 ap157 stress IBL -1.5931423 -1.5019893 -2.1567443 -2.3979742
## 31 ap158 control Salimetrics -0.7916171 -0.7916171 -0.7916171 -1.5994876
## 32 ap159 stress IBL -2.3897325 -2.0543669 -2.0285601 -1.6478369
## 33 ap160 stress IBL -2.2265153 -1.9257415 -1.6815625 -2.1517764
## 34 ap161 stress IBL -1.9316035 -1.8514009 -2.2462647 -1.7829890
## 35 ap162 stress IBL -2.2652393 -1.4458994 -2.6690087 -1.9911317
## 36 ap163 stress IBL -2.2989813 -2.7337744 -2.4937485 -2.1878514
## 37 ap164 stress Salimetrics -1.7203695 -2.1541651 -0.8051967 -1.1147417
## 38 ap165 stress IBL -1.2222183 -1.8178123 -1.7758443 -2.1937541
## 39 ap166 stress Salimetrics -1.4653376 -1.3356012 -2.2349264 -1.3783262
## 40 ap167 stress Salimetrics -1.1711830 -1.6607312 -1.5511690 -1.4312917
## 41 ap168 stress Salimetrics -2.4651040 -2.7030627 -2.4079456 -2.9565116
## 42 ap169 stress Salimetrics -2.4304185 -2.5133061 -2.6310892 -2.2164074
## 43 ap170 stress Salimetrics -1.5994876 -1.2909842 -1.4524342 -1.5371173
## 44 ap171 stress Salimetrics -1.7037486 -1.8388511 -1.8838748 -2.1037342
## 45 ap172 stress Salimetrics -0.7916171 -1.1679624 -1.5371173 -0.7916171
## 46 ap173 stress Salimetrics -2.2256241 -1.9105430 -2.0635682 -2.4534080
## 47 ap174 stress Salimetrics -1.9877744 -2.4889147 -2.4304185 -2.9187712
## T3 day1_change day2_change1 day2_change2 day2_avgchange
## 1 -1.9448570 -27.330286 -27.617421 12.90589444 -7.355763
## 2 -2.4100078 -5.337405 -14.971572 -19.89396051 -17.432766
## 3 -2.1061833 -3.408819 -2.697273 5.32310656 1.312917
## 4 -1.0953175 -5.827912 24.163094 5.68221124 14.922653
## 5 -2.5569301 -26.781330 2.123113 4.86190517 3.492509
## 6 -1.8627824 -31.669377 -4.478951 -1.03396507 -2.756458
## 7 -1.8437190 15.290212 -30.848662 -48.65155378 -39.750108
## 8 -2.4422287 -36.528706 -21.582724 -46.86882757 -34.225776
## 9 -3.0116751 -26.994105 -25.253397 -76.86258346 -51.057990
## 10 -2.3495746 -70.271479 -119.761080 -155.08294897 -137.422014
## 11 -2.3583515 -26.586568 -52.686462 -57.05226896 -54.869365
## 12 -2.3672111 -19.579776 -54.079334 -83.92224989 -69.000792
## 13 -2.5174689 -27.516372 22.769360 -8.69398688 7.037686
## 14 -2.0492755 -3.552623 -4.222132 15.23128878 5.504579
## 15 -2.9786892 -12.027705 -43.417060 -114.51662675 -78.966844
## 16 -1.3470736 -14.668285 22.132424 1.70366360 11.918044
## 17 -0.9545119 19.947944 -23.204701 32.52872667 4.662013
## 18 -1.1679624 -82.938166 -37.173954 17.92012953 -9.626912
## 19 -3.2441936 -8.627593 -27.350400 -43.33501321 -35.342707
## 20 -2.4418472 -4.027984 -5.924439 -63.21316091 -34.568800
## 21 -2.1455813 4.497986 28.815980 20.62406187 24.720021
## 22 -2.3538784 -34.230279 -15.417228 -34.16512455 -24.791176
## 23 -1.0852492 -52.190137 17.128972 19.34582775 18.237400
## 24 -1.6582468 25.626235 16.209145 29.42352925 22.816337
## 25 -2.2211657 -21.104144 -2.665802 25.15443883 11.244318
## 26 -1.9105051 2.692237 -31.941204 -26.02935438 -28.985279
## 27 -1.5069666 -5.513124 -23.787300 29.27621844 2.744459
## 28 -1.1238116 -5.279007 16.459785 43.62680654 30.043296
## 29 -2.1412184 -25.676433 -67.235089 -75.92820766 -71.581648
## 30 -2.1098994 5.721588 -11.184909 2.17201928 -4.506445
## 31 -1.9732813 0.000000 -102.053192 -149.27220433 -125.662698
## 32 -1.8612222 14.033605 18.768149 8.24909772 13.508624
## 33 -1.6971430 13.508721 -27.962911 -0.92654700 -14.444729
## 34 -1.9626313 4.152126 20.624270 12.62689275 16.625581
## 35 -2.6400090 36.170123 25.398083 1.08653558 13.242309
## 36 -2.4927306 -18.912423 12.266556 0.04081674 6.153686
## 37 -2.1455813 -25.215259 -38.443401 -166.46673860 -102.455070
## 38 -2.5385934 -48.730574 -23.533019 -42.95134544 -33.242182
## 39 -0.7916171 8.853682 38.327895 64.57972556 51.453810
## 40 -1.6194882 -41.799465 7.728189 -4.40437140 1.661909
## 41 -2.7488722 -9.653087 -22.781493 -14.15840066 -18.469947
## 42 -1.8515095 -3.410428 15.760840 29.62954271 22.695192
## 43 -1.7602608 19.287640 -5.830425 -21.19384453 -13.512135
## 44 -2.3434071 -7.929720 -11.670599 -24.39293416 -18.031767
## 45 -1.5232602 -47.541330 48.499890 0.90149497 24.700692
## 46 -1.4828053 14.156975 -18.891539 28.14362681 4.626044
## 47 -3.1010928 -25.211127 -20.093361 -27.59501438 -23.844188contrasts(dcort_change$group) = c(-1,1); contrasts(dcort_change$group)## [,1]
## control -1
## stress 1contrasts(dcort_change$assay) = c(-1,1); contrasts(dcort_change$assay) ## [,1]
## IBL -1
## Salimetrics 1dcort_change %>% group_by(subid, group) %>% summarise(n())%>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 23
## 2 stress 24# day 1
summary(lm(day1_change ~ group + assay, data=dcort_change))##
## Call:
## lm(formula = day1_change ~ group + assay, data = dcort_change)
##
## Residuals:
## Min 1Q Median 3Q Max
## -63.805 -13.341 2.472 15.018 43.922
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -13.4423 3.6586 -3.674 0.000644 ***
## group1 5.2944 3.5642 1.485 0.144561
## assay1 -0.3965 3.6652 -0.108 0.914355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24.37 on 44 degrees of freedom
## Multiple R-squared: 0.04775, Adjusted R-squared: 0.004469
## F-statistic: 1.103 on 2 and 44 DF, p-value: 0.3408# day 2
summary(lm(day2_avgchange ~ group + assay, data=dcort_change))##
## Call:
## lm(formula = day2_avgchange ~ group + assay, data = dcort_change)
##
## Residuals:
## Min 1Q Median 3Q Max
## -109.09 -14.70 8.18 26.45 55.01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -15.9413 5.7954 -2.751 0.0086 **
## group1 12.2436 5.6459 2.169 0.0356 *
## assay1 0.1438 5.8059 0.025 0.9804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 38.6 on 44 degrees of freedom
## Multiple R-squared: 0.09715, Adjusted R-squared: 0.05611
## F-statistic: 2.367 on 2 and 44 DF, p-value: 0.1056summary(lm(day2_change1 ~ group + assay, data=dcort_change))##
## Call:
## lm(formula = day2_change1 ~ group + assay, data = dcort_change)
##
## Residuals:
## Min 1Q Median 3Q Max
## -96.226 -19.242 1.691 20.827 49.286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -12.160 4.858 -2.503 0.0161 *
## group1 9.589 4.732 2.026 0.0488 *
## assay1 1.785 4.867 0.367 0.7155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.36 on 44 degrees of freedom
## Multiple R-squared: 0.0903, Adjusted R-squared: 0.04895
## F-statistic: 2.184 on 2 and 44 DF, p-value: 0.1247summary(lm(day2_change2 ~ group + assay, data=dcort_change))##
## Call:
## lm(formula = day2_change2 ~ group + assay, data = dcort_change)
##
## Residuals:
## Min 1Q Median 3Q Max
## -160.145 -19.672 5.498 35.209 70.902
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -19.722 7.551 -2.612 0.0123 *
## group1 14.898 7.357 2.025 0.0489 *
## assay1 -1.498 7.565 -0.198 0.8440
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 50.3 on 44 degrees of freedom
## Multiple R-squared: 0.08532, Adjusted R-squared: 0.04374
## F-statistic: 2.052 on 2 and 44 DF, p-value: 0.1406# day 2: time * group interaction
dcort_combined = dcort_change %>%
dplyr::select(subid, group, assay, day2_change1, day2_change2) %>%
gather(time, cort, day2_change1:day2_change2) %>%
mutate(time = factor(time))
contrasts(dcort_combined$time) = c(-1,1); contrasts(dcort_combined$time)## [,1]
## day2_change1 -1
## day2_change2 1summary(lmer(cort ~ time * group + assay + (1|subid), data=dcort_combined))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: cort ~ time * group + assay + (1 | subid)
## Data: dcort_combined
##
## REML criterion at convergence: 914.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4085 -0.3423 -0.0412 0.4983 1.7982
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 1195.7 34.58
## Residual 588.6 24.26
## Number of obs: 94, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -15.9413 5.7954 44.0000 -2.751 0.0086 **
## time1 -3.3944 2.5028 45.0000 -1.356 0.1818
## group1 12.2436 5.6459 44.0000 2.169 0.0356 *
## assay1 0.1438 5.8059 44.0000 0.025 0.9804
## time1:group1 2.5414 2.5028 45.0000 1.015 0.3153
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) time1 group1 assay1
## time1 0.000
## group1 -0.037 0.000
## assay1 0.236 0.000 -0.071
## time1:grop1 0.000 -0.021 0.000 0.000# day * group interaction
dcort_combined = dcort_change %>%
dplyr::select(subid, group, assay, day1_change, day2_avgchange) %>%
gather(day, cort, day1_change:day2_avgchange) %>%
mutate(day = factor(day))
contrasts(dcort_combined$day) = c(-1,1)
contrasts(dcort_combined$group)## [,1]
## control -1
## stress 1summary(lmer(cort ~ day * group + assay + (1|subid), data=dcort_combined))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: cort ~ day * group + assay + (1 | subid)
## Data: dcort_combined
##
## REML criterion at convergence: 890.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9287 -0.4603 0.1441 0.5752 1.4796
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 240.3 15.50
## Residual 792.6 28.15
## Number of obs: 94, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -14.6918 3.7883 44.0000 -3.878 0.000348 ***
## day1 -1.3131 2.9045 45.0000 -0.452 0.653368
## group1 8.7690 3.6906 44.0000 2.376 0.021918 *
## assay1 -0.1263 3.7951 44.0000 -0.033 0.973597
## day1:group1 3.4932 2.9045 45.0000 1.203 0.235387
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day1 group1 assay1
## day1 0.000
## group1 -0.037 0.000
## assay1 0.236 0.000 -0.071
## day1:group1 0.000 -0.021 0.000 0.000contrasts(dcort_combined$day) = c(0,1); contrasts(dcort_combined$day)## [,1]
## day1_change 0
## day2_avgchange 1summary(lmer(cort ~ day * group + assay+ (1|subid), data=dcort_combined))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: cort ~ day * group + assay + (1 | subid)
## Data: dcort_combined
##
## REML criterion at convergence: 887.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9287 -0.4603 0.1441 0.5752 1.4796
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 240.3 15.50
## Residual 792.6 28.15
## Number of obs: 94, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -13.3787 4.7736 82.9200 -2.803 0.00631 **
## day1 -2.6263 5.8090 45.0000 -0.452 0.65337
## group1 5.2758 4.6964 83.9100 1.123 0.26449
## assay1 -0.1263 3.7951 44.0000 -0.033 0.97360
## day1:group1 6.9864 5.8090 45.0000 1.203 0.23539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day1 group1 assay1
## day1 -0.608
## group1 -0.031 0.013
## assay1 0.187 0.000 -0.056
## day1:group1 0.013 -0.021 -0.618 0.000contrasts(dcort_combined$day) = c(1,0); contrasts(dcort_combined$day)## [,1]
## day1_change 1
## day2_avgchange 0summary(lmer(cort ~ day * group + assay + (1|subid), data=dcort_combined))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: cort ~ day * group + assay + (1 | subid)
## Data: dcort_combined
##
## REML criterion at convergence: 887.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9287 -0.4603 0.1441 0.5752 1.4796
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 240.3 15.50
## Residual 792.6 28.15
## Number of obs: 94, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -16.0049 4.7736 82.9200 -3.353 0.00121 **
## day1 2.6263 5.8090 45.0000 0.452 0.65337
## group1 12.2622 4.6964 83.9100 2.611 0.01069 *
## assay1 -0.1263 3.7951 44.0000 -0.033 0.97360
## day1:group1 -6.9864 5.8090 45.0000 -1.203 0.23539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day1 group1 assay1
## day1 -0.608
## group1 -0.031 0.013
## assay1 0.187 0.000 -0.056
## day1:group1 0.013 -0.021 -0.618 0.000# check out raw levels at T2 and T3
hist(dcort_change$T3)
summary(lm(T2 ~ group + assay, data=dcort_change))##
## Call:
## lm(formula = T2 ~ group + assay, data = dcort_change)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.08512 -0.23595 0.04727 0.28758 1.19079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.9714440 0.0807888 -24.402 <2e-16 ***
## group1 -0.0003406 0.0787055 -0.004 0.997
## assay1 0.1003888 0.0809356 1.240 0.221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5381 on 44 degrees of freedom
## Multiple R-squared: 0.03393, Adjusted R-squared: -0.009979
## F-statistic: 0.7727 on 2 and 44 DF, p-value: 0.4679summary(lm(T3 ~ group + assay, data=dcort_change))##
## Call:
## lm(formula = T3 ~ group + assay, data = dcort_change)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.25186 -0.30628 -0.00228 0.32761 1.10847
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.02524 0.08654 -23.402 <2e-16 ***
## group1 0.10688 0.08431 1.268 0.212
## assay1 0.06913 0.08670 0.797 0.430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5764 on 44 degrees of freedom
## Multiple R-squared: 0.05168, Adjusted R-squared: 0.008573
## F-statistic: 1.199 on 2 and 44 DF, p-value: 0.3112Plot
d_cort = read.csv('/Volumes/group/awagner/sgagnon/AP/data/cortisol/cort_percentchange_t1-2_testbaseline.csv')
str(d_cort)## 'data.frame': 141 obs. of 6 variables:
## $ X : int 0 1 2 3 4 5 6 7 8 9 ...
## $ subid : Factor w/ 47 levels "ap100","ap101",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## $ assay : Factor w/ 2 levels "IBL","Salimetrics": 1 1 1 1 1 1 1 1 1 1 ...
## $ Sample : Factor w/ 3 levels "T1","T2","T3": 1 1 1 1 1 1 1 1 1 1 ...
## $ log.ug.dL.: num 0 0 0 0 0 0 0 0 0 0 ...dfwc = d_cort %>% group_by(group, Sample) %>%
summarise(value = mean(log.ug.dL.),
se = std.error(log.ug.dL.),
n())
dfwc## Source: local data frame [6 x 5]
## Groups: group [?]
##
## # A tibble: 6 x 5
## group Sample value se `n()`
## <fctr> <fctr> <dbl> <dbl> <int>
## 1 control T1 0.000000 0.000000 23
## 2 control T2 -22.292870 7.618383 23
## 3 control T3 -34.164499 11.173649 23
## 4 stress T1 0.000000 0.000000 24
## 5 stress T2 -2.868720 5.522414 24
## 6 stress T3 -4.574591 9.347423 24# dfwc
p3 = ggplot(dfwc, aes(x=Sample, y=value, group=group, color=group)) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=value-se, ymax=value+se),
size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('% Change in cortisol\n(relative to T1)')+
scale_color_manual(values=c('dodgerblue', 'orange')) +
xlab('Sample') +
theme(legend.title=element_blank())
ggsave('~/Experiments/AP/figs/AP_behav_cort.jpeg', p3, dpi=300, width=5, height=4)
p3
0b) Questionnaires:
Do ratings on in-scanner valence questionnaires differ between groups?
## subid BAS.D BAS.F BAS.W BIS SMM TraitAnx group run
## 1 ap100 3.5 3.75 3.2 3.714286 2.75 2.8 control-fmri 1
## 2 ap100 3.5 3.75 3.2 3.714286 2.75 2.8 control-fmri 1
## 3 ap100 3.5 3.75 3.2 3.714286 2.75 2.8 control-fmri 1
## 4 ap100 3.5 3.75 3.2 3.714286 2.75 2.8 control-fmri 2
## 5 ap100 3.5 3.75 3.2 3.714286 2.75 2.8 control-fmri 2
## 6 ap100 3.5 3.75 3.2 3.714286 2.75 2.8 control-fmri 2
## shockCond subScaleName adjustedRating respRT remove good_math
## 1 safe Arousal 1.0 3.45640 NA 5
## 2 safe Negative 1.5 3.91895 NA 5
## 3 safe Positive 2.5 3.09475 NA 5
## 4 threat Arousal 1.0 1.07310 NA 5
## 5 threat Negative 2.5 1.32142 NA 5
## 6 threat Positive 2.5 3.32340 NA 5
## important_math anxious happy safe stressed life_stress sleep modality
## 1 6 3 2 3 3 4 4.5 fmri
## 2 6 3 2 3 3 4 4.5 fmri
## 3 6 3 2 3 3 4 4.5 fmri
## 4 6 3 2 3 3 4 4.5 fmri
## 5 6 3 2 3 3 4 4.5 fmri
## 6 6 3 2 3 3 4 4.5 fmri
## stress_group
## 1 control
## 2 control
## 3 control
## 4 control
## 5 control
## 6 control## subid BAS.D BAS.F BAS.W BIS SMM TraitAnx group run
## 1 ap100 3.50 3.75 3.2 3.714286 2.750 2.80 control-fmri 1
## 2 ap100 3.50 3.75 3.2 3.714286 2.750 2.80 control-fmri 1
## 3 ap100 3.50 3.75 3.2 3.714286 2.750 2.80 control-fmri 2
## 4 ap100 3.50 3.75 3.2 3.714286 2.750 2.80 control-fmri 2
## 5 ap100 3.50 3.75 3.2 3.714286 2.750 2.80 control-fmri 4
## 6 ap100 3.50 3.75 3.2 3.714286 2.750 2.80 control-fmri 4
## 7 ap100 3.50 3.75 3.2 3.714286 2.750 2.80 control-fmri 5
## 8 ap100 3.50 3.75 3.2 3.714286 2.750 2.80 control-fmri 5
## 9 ap100 3.50 3.75 3.2 3.714286 2.750 2.80 control-fmri 6
## 10 ap100 3.50 3.75 3.2 3.714286 2.750 2.80 control-fmri 6
## 11 ap101 3.75 2.00 3.6 3.571429 3.375 1.95 control-fmri 1
## 12 ap101 3.75 2.00 3.6 3.571429 3.375 1.95 control-fmri 1
## 13 ap101 3.75 2.00 3.6 3.571429 3.375 1.95 control-fmri 2
## 14 ap101 3.75 2.00 3.6 3.571429 3.375 1.95 control-fmri 2
## 15 ap101 3.75 2.00 3.6 3.571429 3.375 1.95 control-fmri 3
## 16 ap101 3.75 2.00 3.6 3.571429 3.375 1.95 control-fmri 3
## 17 ap101 3.75 2.00 3.6 3.571429 3.375 1.95 control-fmri 4
## 18 ap101 3.75 2.00 3.6 3.571429 3.375 1.95 control-fmri 4
## 19 ap101 3.75 2.00 3.6 3.571429 3.375 1.95 control-fmri 5
## 20 ap101 3.75 2.00 3.6 3.571429 3.375 1.95 control-fmri 5
## 21 ap101 3.75 2.00 3.6 3.571429 3.375 1.95 control-fmri 6
## 22 ap101 3.75 2.00 3.6 3.571429 3.375 1.95 control-fmri 6
## 23 ap102 3.50 3.75 3.8 2.714286 1.750 2.10 control-fmri 1
## 24 ap102 3.50 3.75 3.8 2.714286 1.750 2.10 control-fmri 1
## 25 ap102 3.50 3.75 3.8 2.714286 1.750 2.10 control-fmri 2
## 26 ap102 3.50 3.75 3.8 2.714286 1.750 2.10 control-fmri 2
## 27 ap102 3.50 3.75 3.8 2.714286 1.750 2.10 control-fmri 3
## 28 ap102 3.50 3.75 3.8 2.714286 1.750 2.10 control-fmri 3
## 29 ap102 3.50 3.75 3.8 2.714286 1.750 2.10 control-fmri 4
## 30 ap102 3.50 3.75 3.8 2.714286 1.750 2.10 control-fmri 4
## 31 ap102 3.50 3.75 3.8 2.714286 1.750 2.10 control-fmri 5
## 32 ap102 3.50 3.75 3.8 2.714286 1.750 2.10 control-fmri 5
## 33 ap102 3.50 3.75 3.8 2.714286 1.750 2.10 control-fmri 6
## 34 ap102 3.50 3.75 3.8 2.714286 1.750 2.10 control-fmri 6
## 35 ap103 2.75 3.25 3.4 2.714286 2.250 1.50 control-fmri 1
## 36 ap103 2.75 3.25 3.4 2.714286 2.250 1.50 control-fmri 1
## 37 ap103 2.75 3.25 3.4 2.714286 2.250 1.50 control-fmri 2
## 38 ap103 2.75 3.25 3.4 2.714286 2.250 1.50 control-fmri 2
## 39 ap103 2.75 3.25 3.4 2.714286 2.250 1.50 control-fmri 3
## 40 ap103 2.75 3.25 3.4 2.714286 2.250 1.50 control-fmri 3
## 41 ap103 2.75 3.25 3.4 2.714286 2.250 1.50 control-fmri 4
## 42 ap103 2.75 3.25 3.4 2.714286 2.250 1.50 control-fmri 4
## 43 ap103 2.75 3.25 3.4 2.714286 2.250 1.50 control-fmri 5
## 44 ap103 2.75 3.25 3.4 2.714286 2.250 1.50 control-fmri 5
## 45 ap103 2.75 3.25 3.4 2.714286 2.250 1.50 control-fmri 6
## 46 ap103 2.75 3.25 3.4 2.714286 2.250 1.50 control-fmri 6
## 47 ap104 2.75 2.75 3.8 3.142857 3.125 2.85 control-fmri 1
## 48 ap104 2.75 2.75 3.8 3.142857 3.125 2.85 control-fmri 1
## 49 ap104 2.75 2.75 3.8 3.142857 3.125 2.85 control-fmri 2
## 50 ap104 2.75 2.75 3.8 3.142857 3.125 2.85 control-fmri 2
## 51 ap104 2.75 2.75 3.8 3.142857 3.125 2.85 control-fmri 3
## 52 ap104 2.75 2.75 3.8 3.142857 3.125 2.85 control-fmri 3
## 53 ap104 2.75 2.75 3.8 3.142857 3.125 2.85 control-fmri 4
## 54 ap104 2.75 2.75 3.8 3.142857 3.125 2.85 control-fmri 4
## 55 ap104 2.75 2.75 3.8 3.142857 3.125 2.85 control-fmri 5
## 56 ap104 2.75 2.75 3.8 3.142857 3.125 2.85 control-fmri 5
## 57 ap104 2.75 2.75 3.8 3.142857 3.125 2.85 control-fmri 6
## 58 ap104 2.75 2.75 3.8 3.142857 3.125 2.85 control-fmri 6
## 59 ap105 2.75 3.00 3.8 3.285714 3.125 2.05 control-fmri 1
## 60 ap105 2.75 3.00 3.8 3.285714 3.125 2.05 control-fmri 1
## 61 ap105 2.75 3.00 3.8 3.285714 3.125 2.05 control-fmri 2
## 62 ap105 2.75 3.00 3.8 3.285714 3.125 2.05 control-fmri 2
## 63 ap105 2.75 3.00 3.8 3.285714 3.125 2.05 control-fmri 3
## 64 ap105 2.75 3.00 3.8 3.285714 3.125 2.05 control-fmri 3
## 65 ap105 2.75 3.00 3.8 3.285714 3.125 2.05 control-fmri 4
## 66 ap105 2.75 3.00 3.8 3.285714 3.125 2.05 control-fmri 4
## 67 ap105 2.75 3.00 3.8 3.285714 3.125 2.05 control-fmri 5
## 68 ap105 2.75 3.00 3.8 3.285714 3.125 2.05 control-fmri 5
## 69 ap105 2.75 3.00 3.8 3.285714 3.125 2.05 control-fmri 6
## 70 ap105 2.75 3.00 3.8 3.285714 3.125 2.05 control-fmri 6
## 71 ap106 2.75 2.75 3.0 2.714286 3.125 2.20 control-fmri 2
## 72 ap106 2.75 2.75 3.0 2.714286 3.125 2.20 control-fmri 2
## 73 ap106 2.75 2.75 3.0 2.714286 3.125 2.20 control-fmri 3
## 74 ap106 2.75 2.75 3.0 2.714286 3.125 2.20 control-fmri 3
## 75 ap106 2.75 2.75 3.0 2.714286 3.125 2.20 control-fmri 4
## 76 ap106 2.75 2.75 3.0 2.714286 3.125 2.20 control-fmri 4
## 77 ap106 2.75 2.75 3.0 2.714286 3.125 2.20 control-fmri 5
## 78 ap106 2.75 2.75 3.0 2.714286 3.125 2.20 control-fmri 5
## 79 ap106 2.75 2.75 3.0 2.714286 3.125 2.20 control-fmri 6
## 80 ap106 2.75 2.75 3.0 2.714286 3.125 2.20 control-fmri 6
## 81 ap107 2.50 3.25 3.6 3.000000 2.375 3.10 control-fmri 1
## 82 ap107 2.50 3.25 3.6 3.000000 2.375 3.10 control-fmri 1
## 83 ap107 2.50 3.25 3.6 3.000000 2.375 3.10 control-fmri 2
## 84 ap107 2.50 3.25 3.6 3.000000 2.375 3.10 control-fmri 2
## 85 ap107 2.50 3.25 3.6 3.000000 2.375 3.10 control-fmri 3
## 86 ap107 2.50 3.25 3.6 3.000000 2.375 3.10 control-fmri 3
## 87 ap107 2.50 3.25 3.6 3.000000 2.375 3.10 control-fmri 4
## 88 ap107 2.50 3.25 3.6 3.000000 2.375 3.10 control-fmri 4
## 89 ap107 2.50 3.25 3.6 3.000000 2.375 3.10 control-fmri 5
## 90 ap107 2.50 3.25 3.6 3.000000 2.375 3.10 control-fmri 5
## 91 ap107 2.50 3.25 3.6 3.000000 2.375 3.10 control-fmri 6
## 92 ap107 2.50 3.25 3.6 3.000000 2.375 3.10 control-fmri 6
## 93 ap108 2.25 2.50 3.0 3.142857 3.125 1.70 control-fmri 1
## 94 ap108 2.25 2.50 3.0 3.142857 3.125 1.70 control-fmri 1
## 95 ap108 2.25 2.50 3.0 3.142857 3.125 1.70 control-fmri 2
## 96 ap108 2.25 2.50 3.0 3.142857 3.125 1.70 control-fmri 2
## 97 ap108 2.25 2.50 3.0 3.142857 3.125 1.70 control-fmri 3
## 98 ap108 2.25 2.50 3.0 3.142857 3.125 1.70 control-fmri 3
## 99 ap108 2.25 2.50 3.0 3.142857 3.125 1.70 control-fmri 4
## 100 ap108 2.25 2.50 3.0 3.142857 3.125 1.70 control-fmri 4
## 101 ap108 2.25 2.50 3.0 3.142857 3.125 1.70 control-fmri 5
## 102 ap108 2.25 2.50 3.0 3.142857 3.125 1.70 control-fmri 5
## 103 ap108 2.25 2.50 3.0 3.142857 3.125 1.70 control-fmri 6
## 104 ap108 2.25 2.50 3.0 3.142857 3.125 1.70 control-fmri 6
## 105 ap109 4.00 3.75 3.8 2.571429 1.500 2.05 control-fmri 1
## 106 ap109 4.00 3.75 3.8 2.571429 1.500 2.05 control-fmri 1
## 107 ap109 4.00 3.75 3.8 2.571429 1.500 2.05 control-fmri 2
## 108 ap109 4.00 3.75 3.8 2.571429 1.500 2.05 control-fmri 2
## 109 ap109 4.00 3.75 3.8 2.571429 1.500 2.05 control-fmri 3
## 110 ap109 4.00 3.75 3.8 2.571429 1.500 2.05 control-fmri 3
## 111 ap109 4.00 3.75 3.8 2.571429 1.500 2.05 control-fmri 4
## 112 ap109 4.00 3.75 3.8 2.571429 1.500 2.05 control-fmri 4
## 113 ap109 4.00 3.75 3.8 2.571429 1.500 2.05 control-fmri 5
## 114 ap109 4.00 3.75 3.8 2.571429 1.500 2.05 control-fmri 5
## 115 ap109 4.00 3.75 3.8 2.571429 1.500 2.05 control-fmri 6
## 116 ap109 4.00 3.75 3.8 2.571429 1.500 2.05 control-fmri 6
## 117 ap110 3.00 2.25 3.4 2.285714 2.500 1.55 control-fmri 1
## 118 ap110 3.00 2.25 3.4 2.285714 2.500 1.55 control-fmri 1
## 119 ap110 3.00 2.25 3.4 2.285714 2.500 1.55 control-fmri 2
## 120 ap110 3.00 2.25 3.4 2.285714 2.500 1.55 control-fmri 2
## 121 ap110 3.00 2.25 3.4 2.285714 2.500 1.55 control-fmri 3
## 122 ap110 3.00 2.25 3.4 2.285714 2.500 1.55 control-fmri 3
## 123 ap110 3.00 2.25 3.4 2.285714 2.500 1.55 control-fmri 4
## 124 ap110 3.00 2.25 3.4 2.285714 2.500 1.55 control-fmri 4
## 125 ap110 3.00 2.25 3.4 2.285714 2.500 1.55 control-fmri 5
## 126 ap110 3.00 2.25 3.4 2.285714 2.500 1.55 control-fmri 5
## 127 ap110 3.00 2.25 3.4 2.285714 2.500 1.55 control-fmri 6
## 128 ap110 3.00 2.25 3.4 2.285714 2.500 1.55 control-fmri 6
## 129 ap111 2.75 3.25 3.2 2.714286 2.375 2.50 control-fmri 1
## 130 ap111 2.75 3.25 3.2 2.714286 2.375 2.50 control-fmri 1
## 131 ap111 2.75 3.25 3.2 2.714286 2.375 2.50 control-fmri 2
## 132 ap111 2.75 3.25 3.2 2.714286 2.375 2.50 control-fmri 2
## 133 ap111 2.75 3.25 3.2 2.714286 2.375 2.50 control-fmri 3
## 134 ap111 2.75 3.25 3.2 2.714286 2.375 2.50 control-fmri 3
## 135 ap111 2.75 3.25 3.2 2.714286 2.375 2.50 control-fmri 4
## 136 ap111 2.75 3.25 3.2 2.714286 2.375 2.50 control-fmri 4
## 137 ap111 2.75 3.25 3.2 2.714286 2.375 2.50 control-fmri 5
## 138 ap111 2.75 3.25 3.2 2.714286 2.375 2.50 control-fmri 5
## 139 ap111 2.75 3.25 3.2 2.714286 2.375 2.50 control-fmri 6
## 140 ap111 2.75 3.25 3.2 2.714286 2.375 2.50 control-fmri 6
## 141 ap113 3.25 3.50 3.6 3.000000 3.250 2.95 control-fmri 1
## 142 ap113 3.25 3.50 3.6 3.000000 3.250 2.95 control-fmri 1
## 143 ap113 3.25 3.50 3.6 3.000000 3.250 2.95 control-fmri 2
## 144 ap113 3.25 3.50 3.6 3.000000 3.250 2.95 control-fmri 2
## 145 ap113 3.25 3.50 3.6 3.000000 3.250 2.95 control-fmri 3
## 146 ap113 3.25 3.50 3.6 3.000000 3.250 2.95 control-fmri 3
## 147 ap113 3.25 3.50 3.6 3.000000 3.250 2.95 control-fmri 4
## 148 ap113 3.25 3.50 3.6 3.000000 3.250 2.95 control-fmri 4
## 149 ap113 3.25 3.50 3.6 3.000000 3.250 2.95 control-fmri 5
## 150 ap113 3.25 3.50 3.6 3.000000 3.250 2.95 control-fmri 5
## 151 ap113 3.25 3.50 3.6 3.000000 3.250 2.95 control-fmri 6
## 152 ap113 3.25 3.50 3.6 3.000000 3.250 2.95 control-fmri 6
## 153 ap114 3.25 3.50 4.0 2.285714 3.750 1.70 control-fmri 1
## 154 ap114 3.25 3.50 4.0 2.285714 3.750 1.70 control-fmri 1
## 155 ap114 3.25 3.50 4.0 2.285714 3.750 1.70 control-fmri 2
## 156 ap114 3.25 3.50 4.0 2.285714 3.750 1.70 control-fmri 2
## 157 ap114 3.25 3.50 4.0 2.285714 3.750 1.70 control-fmri 3
## 158 ap114 3.25 3.50 4.0 2.285714 3.750 1.70 control-fmri 3
## 159 ap114 3.25 3.50 4.0 2.285714 3.750 1.70 control-fmri 4
## 160 ap114 3.25 3.50 4.0 2.285714 3.750 1.70 control-fmri 4
## 161 ap114 3.25 3.50 4.0 2.285714 3.750 1.70 control-fmri 5
## 162 ap114 3.25 3.50 4.0 2.285714 3.750 1.70 control-fmri 5
## 163 ap114 3.25 3.50 4.0 2.285714 3.750 1.70 control-fmri 6
## 164 ap114 3.25 3.50 4.0 2.285714 3.750 1.70 control-fmri 6
## 165 ap115 4.00 3.50 4.0 2.428571 3.000 1.65 control-fmri 1
## 166 ap115 4.00 3.50 4.0 2.428571 3.000 1.65 control-fmri 1
## 167 ap115 4.00 3.50 4.0 2.428571 3.000 1.65 control-fmri 2
## 168 ap115 4.00 3.50 4.0 2.428571 3.000 1.65 control-fmri 2
## 169 ap115 4.00 3.50 4.0 2.428571 3.000 1.65 control-fmri 3
## 170 ap115 4.00 3.50 4.0 2.428571 3.000 1.65 control-fmri 3
## 171 ap115 4.00 3.50 4.0 2.428571 3.000 1.65 control-fmri 4
## 172 ap115 4.00 3.50 4.0 2.428571 3.000 1.65 control-fmri 4
## 173 ap115 4.00 3.50 4.0 2.428571 3.000 1.65 control-fmri 5
## 174 ap115 4.00 3.50 4.0 2.428571 3.000 1.65 control-fmri 5
## 175 ap115 4.00 3.50 4.0 2.428571 3.000 1.65 control-fmri 6
## 176 ap115 4.00 3.50 4.0 2.428571 3.000 1.65 control-fmri 6
## 177 ap116 3.00 3.25 3.8 3.428571 3.500 1.95 control-fmri 1
## 178 ap116 3.00 3.25 3.8 3.428571 3.500 1.95 control-fmri 1
## 179 ap116 3.00 3.25 3.8 3.428571 3.500 1.95 control-fmri 2
## 180 ap116 3.00 3.25 3.8 3.428571 3.500 1.95 control-fmri 2
## 181 ap116 3.00 3.25 3.8 3.428571 3.500 1.95 control-fmri 3
## 182 ap116 3.00 3.25 3.8 3.428571 3.500 1.95 control-fmri 3
## 183 ap116 3.00 3.25 3.8 3.428571 3.500 1.95 control-fmri 4
## 184 ap116 3.00 3.25 3.8 3.428571 3.500 1.95 control-fmri 4
## 185 ap116 3.00 3.25 3.8 3.428571 3.500 1.95 control-fmri 5
## 186 ap116 3.00 3.25 3.8 3.428571 3.500 1.95 control-fmri 5
## 187 ap116 3.00 3.25 3.8 3.428571 3.500 1.95 control-fmri 6
## 188 ap116 3.00 3.25 3.8 3.428571 3.500 1.95 control-fmri 6
## 189 ap117 3.75 4.00 4.0 2.428571 3.625 2.20 control-fmri 1
## 190 ap117 3.75 4.00 4.0 2.428571 3.625 2.20 control-fmri 1
## 191 ap117 3.75 4.00 4.0 2.428571 3.625 2.20 control-fmri 2
## 192 ap117 3.75 4.00 4.0 2.428571 3.625 2.20 control-fmri 2
## 193 ap117 3.75 4.00 4.0 2.428571 3.625 2.20 control-fmri 3
## 194 ap117 3.75 4.00 4.0 2.428571 3.625 2.20 control-fmri 3
## 195 ap117 3.75 4.00 4.0 2.428571 3.625 2.20 control-fmri 4
## 196 ap117 3.75 4.00 4.0 2.428571 3.625 2.20 control-fmri 4
## 197 ap117 3.75 4.00 4.0 2.428571 3.625 2.20 control-fmri 5
## 198 ap117 3.75 4.00 4.0 2.428571 3.625 2.20 control-fmri 5
## 199 ap117 3.75 4.00 4.0 2.428571 3.625 2.20 control-fmri 6
## 200 ap117 3.75 4.00 4.0 2.428571 3.625 2.20 control-fmri 6
## 201 ap118 3.00 3.50 3.2 2.142857 2.875 2.05 control-fmri 1
## 202 ap118 3.00 3.50 3.2 2.142857 2.875 2.05 control-fmri 1
## 203 ap118 3.00 3.50 3.2 2.142857 2.875 2.05 control-fmri 2
## 204 ap118 3.00 3.50 3.2 2.142857 2.875 2.05 control-fmri 2
## 205 ap118 3.00 3.50 3.2 2.142857 2.875 2.05 control-fmri 3
## 206 ap118 3.00 3.50 3.2 2.142857 2.875 2.05 control-fmri 3
## 207 ap118 3.00 3.50 3.2 2.142857 2.875 2.05 control-fmri 4
## 208 ap118 3.00 3.50 3.2 2.142857 2.875 2.05 control-fmri 4
## 209 ap118 3.00 3.50 3.2 2.142857 2.875 2.05 control-fmri 5
## 210 ap118 3.00 3.50 3.2 2.142857 2.875 2.05 control-fmri 5
## 211 ap118 3.00 3.50 3.2 2.142857 2.875 2.05 control-fmri 6
## 212 ap118 3.00 3.50 3.2 2.142857 2.875 2.05 control-fmri 6
## 213 ap119 2.75 2.00 3.0 2.857143 3.250 1.80 control-fmri 1
## 214 ap119 2.75 2.00 3.0 2.857143 3.250 1.80 control-fmri 1
## 215 ap119 2.75 2.00 3.0 2.857143 3.250 1.80 control-fmri 2
## 216 ap119 2.75 2.00 3.0 2.857143 3.250 1.80 control-fmri 2
## 217 ap119 2.75 2.00 3.0 2.857143 3.250 1.80 control-fmri 3
## 218 ap119 2.75 2.00 3.0 2.857143 3.250 1.80 control-fmri 3
## 219 ap119 2.75 2.00 3.0 2.857143 3.250 1.80 control-fmri 4
## 220 ap119 2.75 2.00 3.0 2.857143 3.250 1.80 control-fmri 4
## 221 ap119 2.75 2.00 3.0 2.857143 3.250 1.80 control-fmri 5
## 222 ap119 2.75 2.00 3.0 2.857143 3.250 1.80 control-fmri 5
## 223 ap119 2.75 2.00 3.0 2.857143 3.250 1.80 control-fmri 6
## 224 ap119 2.75 2.00 3.0 2.857143 3.250 1.80 control-fmri 6
## 225 ap120 3.50 3.50 3.8 2.142857 2.875 1.55 control-fmri 1
## 226 ap120 3.50 3.50 3.8 2.142857 2.875 1.55 control-fmri 1
## 227 ap120 3.50 3.50 3.8 2.142857 2.875 1.55 control-fmri 2
## 228 ap120 3.50 3.50 3.8 2.142857 2.875 1.55 control-fmri 2
## 229 ap120 3.50 3.50 3.8 2.142857 2.875 1.55 control-fmri 3
## 230 ap120 3.50 3.50 3.8 2.142857 2.875 1.55 control-fmri 3
## 231 ap120 3.50 3.50 3.8 2.142857 2.875 1.55 control-fmri 4
## 232 ap120 3.50 3.50 3.8 2.142857 2.875 1.55 control-fmri 4
## 233 ap120 3.50 3.50 3.8 2.142857 2.875 1.55 control-fmri 5
## 234 ap120 3.50 3.50 3.8 2.142857 2.875 1.55 control-fmri 5
## 235 ap120 3.50 3.50 3.8 2.142857 2.875 1.55 control-fmri 6
## 236 ap120 3.50 3.50 3.8 2.142857 2.875 1.55 control-fmri 6
## 237 ap121 4.00 3.75 3.8 2.142857 3.375 1.80 control-fmri 1
## 238 ap121 4.00 3.75 3.8 2.142857 3.375 1.80 control-fmri 1
## 239 ap121 4.00 3.75 3.8 2.142857 3.375 1.80 control-fmri 2
## 240 ap121 4.00 3.75 3.8 2.142857 3.375 1.80 control-fmri 2
## 241 ap121 4.00 3.75 3.8 2.142857 3.375 1.80 control-fmri 3
## 242 ap121 4.00 3.75 3.8 2.142857 3.375 1.80 control-fmri 3
## 243 ap121 4.00 3.75 3.8 2.142857 3.375 1.80 control-fmri 4
## 244 ap121 4.00 3.75 3.8 2.142857 3.375 1.80 control-fmri 4
## 245 ap121 4.00 3.75 3.8 2.142857 3.375 1.80 control-fmri 5
## 246 ap121 4.00 3.75 3.8 2.142857 3.375 1.80 control-fmri 5
## 247 ap121 4.00 3.75 3.8 2.142857 3.375 1.80 control-fmri 6
## 248 ap121 4.00 3.75 3.8 2.142857 3.375 1.80 control-fmri 6
## 249 ap122 2.50 2.50 3.6 3.571429 3.375 2.55 control-fmri 1
## 250 ap122 2.50 2.50 3.6 3.571429 3.375 2.55 control-fmri 1
## 251 ap122 2.50 2.50 3.6 3.571429 3.375 2.55 control-fmri 2
## 252 ap122 2.50 2.50 3.6 3.571429 3.375 2.55 control-fmri 2
## 253 ap122 2.50 2.50 3.6 3.571429 3.375 2.55 control-fmri 3
## 254 ap122 2.50 2.50 3.6 3.571429 3.375 2.55 control-fmri 3
## 255 ap122 2.50 2.50 3.6 3.571429 3.375 2.55 control-fmri 4
## 256 ap122 2.50 2.50 3.6 3.571429 3.375 2.55 control-fmri 4
## 257 ap122 2.50 2.50 3.6 3.571429 3.375 2.55 control-fmri 5
## 258 ap122 2.50 2.50 3.6 3.571429 3.375 2.55 control-fmri 5
## 259 ap122 2.50 2.50 3.6 3.571429 3.375 2.55 control-fmri 6
## 260 ap122 2.50 2.50 3.6 3.571429 3.375 2.55 control-fmri 6
## 261 ap150 2.50 3.00 3.0 2.571429 2.125 2.60 stress-fmri 1
## 262 ap150 2.50 3.00 3.0 2.571429 2.125 2.60 stress-fmri 1
## 263 ap150 2.50 3.00 3.0 2.571429 2.125 2.60 stress-fmri 2
## 264 ap150 2.50 3.00 3.0 2.571429 2.125 2.60 stress-fmri 2
## 265 ap150 2.50 3.00 3.0 2.571429 2.125 2.60 stress-fmri 3
## 266 ap150 2.50 3.00 3.0 2.571429 2.125 2.60 stress-fmri 3
## 267 ap150 2.50 3.00 3.0 2.571429 2.125 2.60 stress-fmri 4
## 268 ap150 2.50 3.00 3.0 2.571429 2.125 2.60 stress-fmri 4
## 269 ap150 2.50 3.00 3.0 2.571429 2.125 2.60 stress-fmri 5
## 270 ap150 2.50 3.00 3.0 2.571429 2.125 2.60 stress-fmri 5
## 271 ap150 2.50 3.00 3.0 2.571429 2.125 2.60 stress-fmri 6
## 272 ap150 2.50 3.00 3.0 2.571429 2.125 2.60 stress-fmri 6
## 273 ap151 2.25 2.50 2.6 2.857143 3.375 2.40 stress-fmri 1
## 274 ap151 2.25 2.50 2.6 2.857143 3.375 2.40 stress-fmri 1
## 275 ap151 2.25 2.50 2.6 2.857143 3.375 2.40 stress-fmri 2
## 276 ap151 2.25 2.50 2.6 2.857143 3.375 2.40 stress-fmri 2
## 277 ap151 2.25 2.50 2.6 2.857143 3.375 2.40 stress-fmri 3
## 278 ap151 2.25 2.50 2.6 2.857143 3.375 2.40 stress-fmri 3
## 279 ap151 2.25 2.50 2.6 2.857143 3.375 2.40 stress-fmri 4
## 280 ap151 2.25 2.50 2.6 2.857143 3.375 2.40 stress-fmri 4
## 281 ap151 2.25 2.50 2.6 2.857143 3.375 2.40 stress-fmri 5
## 282 ap151 2.25 2.50 2.6 2.857143 3.375 2.40 stress-fmri 5
## 283 ap151 2.25 2.50 2.6 2.857143 3.375 2.40 stress-fmri 6
## 284 ap151 2.25 2.50 2.6 2.857143 3.375 2.40 stress-fmri 6
## 285 ap152 3.25 3.00 3.2 2.285714 3.500 1.95 stress-fmri 1
## 286 ap152 3.25 3.00 3.2 2.285714 3.500 1.95 stress-fmri 1
## 287 ap152 3.25 3.00 3.2 2.285714 3.500 1.95 stress-fmri 2
## 288 ap152 3.25 3.00 3.2 2.285714 3.500 1.95 stress-fmri 2
## 289 ap152 3.25 3.00 3.2 2.285714 3.500 1.95 stress-fmri 3
## 290 ap152 3.25 3.00 3.2 2.285714 3.500 1.95 stress-fmri 3
## 291 ap152 3.25 3.00 3.2 2.285714 3.500 1.95 stress-fmri 4
## 292 ap152 3.25 3.00 3.2 2.285714 3.500 1.95 stress-fmri 4
## 293 ap152 3.25 3.00 3.2 2.285714 3.500 1.95 stress-fmri 5
## 294 ap152 3.25 3.00 3.2 2.285714 3.500 1.95 stress-fmri 5
## 295 ap152 3.25 3.00 3.2 2.285714 3.500 1.95 stress-fmri 6
## 296 ap152 3.25 3.00 3.2 2.285714 3.500 1.95 stress-fmri 6
## 297 ap153 2.50 3.50 2.8 2.285714 2.000 1.55 stress-fmri 1
## 298 ap153 2.50 3.50 2.8 2.285714 2.000 1.55 stress-fmri 1
## 299 ap153 2.50 3.50 2.8 2.285714 2.000 1.55 stress-fmri 2
## 300 ap153 2.50 3.50 2.8 2.285714 2.000 1.55 stress-fmri 2
## 301 ap153 2.50 3.50 2.8 2.285714 2.000 1.55 stress-fmri 3
## 302 ap153 2.50 3.50 2.8 2.285714 2.000 1.55 stress-fmri 3
## 303 ap153 2.50 3.50 2.8 2.285714 2.000 1.55 stress-fmri 4
## 304 ap153 2.50 3.50 2.8 2.285714 2.000 1.55 stress-fmri 4
## 305 ap153 2.50 3.50 2.8 2.285714 2.000 1.55 stress-fmri 5
## 306 ap153 2.50 3.50 2.8 2.285714 2.000 1.55 stress-fmri 5
## 307 ap153 2.50 3.50 2.8 2.285714 2.000 1.55 stress-fmri 6
## 308 ap153 2.50 3.50 2.8 2.285714 2.000 1.55 stress-fmri 6
## 309 ap154 2.25 2.50 3.4 2.857143 3.750 1.85 stress-fmri 1
## 310 ap154 2.25 2.50 3.4 2.857143 3.750 1.85 stress-fmri 1
## 311 ap154 2.25 2.50 3.4 2.857143 3.750 1.85 stress-fmri 2
## 312 ap154 2.25 2.50 3.4 2.857143 3.750 1.85 stress-fmri 2
## 313 ap154 2.25 2.50 3.4 2.857143 3.750 1.85 stress-fmri 3
## 314 ap154 2.25 2.50 3.4 2.857143 3.750 1.85 stress-fmri 3
## 315 ap154 2.25 2.50 3.4 2.857143 3.750 1.85 stress-fmri 4
## 316 ap154 2.25 2.50 3.4 2.857143 3.750 1.85 stress-fmri 4
## 317 ap154 2.25 2.50 3.4 2.857143 3.750 1.85 stress-fmri 5
## 318 ap154 2.25 2.50 3.4 2.857143 3.750 1.85 stress-fmri 5
## 319 ap154 2.25 2.50 3.4 2.857143 3.750 1.85 stress-fmri 6
## 320 ap154 2.25 2.50 3.4 2.857143 3.750 1.85 stress-fmri 6
## 321 ap155 3.75 3.50 4.0 2.142857 3.625 1.30 stress-fmri 1
## 322 ap155 3.75 3.50 4.0 2.142857 3.625 1.30 stress-fmri 1
## 323 ap155 3.75 3.50 4.0 2.142857 3.625 1.30 stress-fmri 2
## 324 ap155 3.75 3.50 4.0 2.142857 3.625 1.30 stress-fmri 2
## 325 ap155 3.75 3.50 4.0 2.142857 3.625 1.30 stress-fmri 3
## 326 ap155 3.75 3.50 4.0 2.142857 3.625 1.30 stress-fmri 3
## 327 ap155 3.75 3.50 4.0 2.142857 3.625 1.30 stress-fmri 4
## 328 ap155 3.75 3.50 4.0 2.142857 3.625 1.30 stress-fmri 4
## 329 ap155 3.75 3.50 4.0 2.142857 3.625 1.30 stress-fmri 5
## 330 ap155 3.75 3.50 4.0 2.142857 3.625 1.30 stress-fmri 5
## 331 ap156 3.75 2.75 3.8 2.428571 3.750 1.55 stress-fmri 1
## 332 ap156 3.75 2.75 3.8 2.428571 3.750 1.55 stress-fmri 1
## 333 ap156 3.75 2.75 3.8 2.428571 3.750 1.55 stress-fmri 2
## 334 ap156 3.75 2.75 3.8 2.428571 3.750 1.55 stress-fmri 2
## 335 ap156 3.75 2.75 3.8 2.428571 3.750 1.55 stress-fmri 3
## 336 ap156 3.75 2.75 3.8 2.428571 3.750 1.55 stress-fmri 3
## 337 ap156 3.75 2.75 3.8 2.428571 3.750 1.55 stress-fmri 4
## 338 ap156 3.75 2.75 3.8 2.428571 3.750 1.55 stress-fmri 4
## 339 ap156 3.75 2.75 3.8 2.428571 3.750 1.55 stress-fmri 5
## 340 ap156 3.75 2.75 3.8 2.428571 3.750 1.55 stress-fmri 5
## 341 ap156 3.75 2.75 3.8 2.428571 3.750 1.55 stress-fmri 6
## 342 ap156 3.75 2.75 3.8 2.428571 3.750 1.55 stress-fmri 6
## 343 ap157 2.50 3.75 3.4 2.571429 2.500 1.40 stress-fmri 1
## 344 ap157 2.50 3.75 3.4 2.571429 2.500 1.40 stress-fmri 1
## 345 ap157 2.50 3.75 3.4 2.571429 2.500 1.40 stress-fmri 2
## 346 ap157 2.50 3.75 3.4 2.571429 2.500 1.40 stress-fmri 2
## 347 ap157 2.50 3.75 3.4 2.571429 2.500 1.40 stress-fmri 3
## 348 ap157 2.50 3.75 3.4 2.571429 2.500 1.40 stress-fmri 3
## 349 ap157 2.50 3.75 3.4 2.571429 2.500 1.40 stress-fmri 4
## 350 ap157 2.50 3.75 3.4 2.571429 2.500 1.40 stress-fmri 4
## 351 ap157 2.50 3.75 3.4 2.571429 2.500 1.40 stress-fmri 5
## 352 ap157 2.50 3.75 3.4 2.571429 2.500 1.40 stress-fmri 5
## 353 ap157 2.50 3.75 3.4 2.571429 2.500 1.40 stress-fmri 6
## 354 ap157 2.50 3.75 3.4 2.571429 2.500 1.40 stress-fmri 6
## 355 ap158 2.75 2.75 3.4 3.142857 3.375 1.50 control-fmri 1
## 356 ap158 2.75 2.75 3.4 3.142857 3.375 1.50 control-fmri 1
## 357 ap158 2.75 2.75 3.4 3.142857 3.375 1.50 control-fmri 2
## 358 ap158 2.75 2.75 3.4 3.142857 3.375 1.50 control-fmri 2
## 359 ap158 2.75 2.75 3.4 3.142857 3.375 1.50 control-fmri 3
## 360 ap158 2.75 2.75 3.4 3.142857 3.375 1.50 control-fmri 3
## 361 ap158 2.75 2.75 3.4 3.142857 3.375 1.50 control-fmri 4
## 362 ap158 2.75 2.75 3.4 3.142857 3.375 1.50 control-fmri 4
## 363 ap158 2.75 2.75 3.4 3.142857 3.375 1.50 control-fmri 5
## 364 ap158 2.75 2.75 3.4 3.142857 3.375 1.50 control-fmri 5
## 365 ap158 2.75 2.75 3.4 3.142857 3.375 1.50 control-fmri 6
## 366 ap158 2.75 2.75 3.4 3.142857 3.375 1.50 control-fmri 6
## 367 ap159 3.50 3.00 4.0 3.571429 1.875 3.25 stress-fmri 1
## 368 ap159 3.50 3.00 4.0 3.571429 1.875 3.25 stress-fmri 1
## 369 ap159 3.50 3.00 4.0 3.571429 1.875 3.25 stress-fmri 2
## 370 ap159 3.50 3.00 4.0 3.571429 1.875 3.25 stress-fmri 2
## 371 ap159 3.50 3.00 4.0 3.571429 1.875 3.25 stress-fmri 3
## 372 ap159 3.50 3.00 4.0 3.571429 1.875 3.25 stress-fmri 3
## 373 ap159 3.50 3.00 4.0 3.571429 1.875 3.25 stress-fmri 4
## 374 ap159 3.50 3.00 4.0 3.571429 1.875 3.25 stress-fmri 4
## 375 ap159 3.50 3.00 4.0 3.571429 1.875 3.25 stress-fmri 5
## 376 ap159 3.50 3.00 4.0 3.571429 1.875 3.25 stress-fmri 5
## 377 ap159 3.50 3.00 4.0 3.571429 1.875 3.25 stress-fmri 6
## 378 ap159 3.50 3.00 4.0 3.571429 1.875 3.25 stress-fmri 6
## 379 ap160 3.00 3.25 3.8 3.571429 3.000 1.95 stress-fmri 1
## 380 ap160 3.00 3.25 3.8 3.571429 3.000 1.95 stress-fmri 1
## 381 ap160 3.00 3.25 3.8 3.571429 3.000 1.95 stress-fmri 2
## 382 ap160 3.00 3.25 3.8 3.571429 3.000 1.95 stress-fmri 2
## 383 ap160 3.00 3.25 3.8 3.571429 3.000 1.95 stress-fmri 3
## 384 ap160 3.00 3.25 3.8 3.571429 3.000 1.95 stress-fmri 3
## 385 ap160 3.00 3.25 3.8 3.571429 3.000 1.95 stress-fmri 4
## 386 ap160 3.00 3.25 3.8 3.571429 3.000 1.95 stress-fmri 4
## 387 ap160 3.00 3.25 3.8 3.571429 3.000 1.95 stress-fmri 5
## 388 ap160 3.00 3.25 3.8 3.571429 3.000 1.95 stress-fmri 5
## 389 ap160 3.00 3.25 3.8 3.571429 3.000 1.95 stress-fmri 6
## 390 ap160 3.00 3.25 3.8 3.571429 3.000 1.95 stress-fmri 6
## 391 ap161 3.00 3.25 4.0 3.571429 2.625 1.95 stress-fmri 1
## 392 ap161 3.00 3.25 4.0 3.571429 2.625 1.95 stress-fmri 1
## 393 ap161 3.00 3.25 4.0 3.571429 2.625 1.95 stress-fmri 2
## 394 ap161 3.00 3.25 4.0 3.571429 2.625 1.95 stress-fmri 2
## 395 ap161 3.00 3.25 4.0 3.571429 2.625 1.95 stress-fmri 3
## 396 ap161 3.00 3.25 4.0 3.571429 2.625 1.95 stress-fmri 3
## 397 ap161 3.00 3.25 4.0 3.571429 2.625 1.95 stress-fmri 4
## 398 ap161 3.00 3.25 4.0 3.571429 2.625 1.95 stress-fmri 4
## 399 ap161 3.00 3.25 4.0 3.571429 2.625 1.95 stress-fmri 5
## 400 ap161 3.00 3.25 4.0 3.571429 2.625 1.95 stress-fmri 5
## 401 ap161 3.00 3.25 4.0 3.571429 2.625 1.95 stress-fmri 6
## 402 ap161 3.00 3.25 4.0 3.571429 2.625 1.95 stress-fmri 6
## 403 ap162 3.00 2.75 3.4 3.000000 3.625 2.10 stress-fmri 1
## 404 ap162 3.00 2.75 3.4 3.000000 3.625 2.10 stress-fmri 1
## 405 ap162 3.00 2.75 3.4 3.000000 3.625 2.10 stress-fmri 2
## 406 ap162 3.00 2.75 3.4 3.000000 3.625 2.10 stress-fmri 2
## 407 ap162 3.00 2.75 3.4 3.000000 3.625 2.10 stress-fmri 3
## 408 ap162 3.00 2.75 3.4 3.000000 3.625 2.10 stress-fmri 3
## 409 ap162 3.00 2.75 3.4 3.000000 3.625 2.10 stress-fmri 4
## 410 ap162 3.00 2.75 3.4 3.000000 3.625 2.10 stress-fmri 4
## 411 ap162 3.00 2.75 3.4 3.000000 3.625 2.10 stress-fmri 5
## 412 ap162 3.00 2.75 3.4 3.000000 3.625 2.10 stress-fmri 5
## 413 ap162 3.00 2.75 3.4 3.000000 3.625 2.10 stress-fmri 6
## 414 ap162 3.00 2.75 3.4 3.000000 3.625 2.10 stress-fmri 6
## 415 ap163 2.50 3.00 3.4 2.857143 3.000 2.10 stress-fmri 1
## 416 ap163 2.50 3.00 3.4 2.857143 3.000 2.10 stress-fmri 1
## 417 ap163 2.50 3.00 3.4 2.857143 3.000 2.10 stress-fmri 2
## 418 ap163 2.50 3.00 3.4 2.857143 3.000 2.10 stress-fmri 2
## 419 ap163 2.50 3.00 3.4 2.857143 3.000 2.10 stress-fmri 3
## 420 ap163 2.50 3.00 3.4 2.857143 3.000 2.10 stress-fmri 3
## 421 ap163 2.50 3.00 3.4 2.857143 3.000 2.10 stress-fmri 4
## 422 ap163 2.50 3.00 3.4 2.857143 3.000 2.10 stress-fmri 4
## 423 ap163 2.50 3.00 3.4 2.857143 3.000 2.10 stress-fmri 5
## 424 ap163 2.50 3.00 3.4 2.857143 3.000 2.10 stress-fmri 5
## 425 ap163 2.50 3.00 3.4 2.857143 3.000 2.10 stress-fmri 6
## 426 ap163 2.50 3.00 3.4 2.857143 3.000 2.10 stress-fmri 6
## 427 ap164 4.00 4.00 3.4 3.285714 2.875 2.75 stress-fmri 1
## 428 ap164 4.00 4.00 3.4 3.285714 2.875 2.75 stress-fmri 1
## 429 ap164 4.00 4.00 3.4 3.285714 2.875 2.75 stress-fmri 2
## 430 ap164 4.00 4.00 3.4 3.285714 2.875 2.75 stress-fmri 2
## 431 ap164 4.00 4.00 3.4 3.285714 2.875 2.75 stress-fmri 3
## 432 ap164 4.00 4.00 3.4 3.285714 2.875 2.75 stress-fmri 3
## 433 ap164 4.00 4.00 3.4 3.285714 2.875 2.75 stress-fmri 4
## 434 ap164 4.00 4.00 3.4 3.285714 2.875 2.75 stress-fmri 4
## 435 ap164 4.00 4.00 3.4 3.285714 2.875 2.75 stress-fmri 5
## 436 ap164 4.00 4.00 3.4 3.285714 2.875 2.75 stress-fmri 5
## 437 ap164 4.00 4.00 3.4 3.285714 2.875 2.75 stress-fmri 6
## 438 ap164 4.00 4.00 3.4 3.285714 2.875 2.75 stress-fmri 6
## 439 ap165 3.75 2.50 3.8 3.857143 3.250 1.80 stress-fmri 1
## 440 ap165 3.75 2.50 3.8 3.857143 3.250 1.80 stress-fmri 1
## 441 ap165 3.75 2.50 3.8 3.857143 3.250 1.80 stress-fmri 2
## 442 ap165 3.75 2.50 3.8 3.857143 3.250 1.80 stress-fmri 2
## 443 ap165 3.75 2.50 3.8 3.857143 3.250 1.80 stress-fmri 3
## 444 ap165 3.75 2.50 3.8 3.857143 3.250 1.80 stress-fmri 3
## 445 ap165 3.75 2.50 3.8 3.857143 3.250 1.80 stress-fmri 4
## 446 ap165 3.75 2.50 3.8 3.857143 3.250 1.80 stress-fmri 4
## 447 ap165 3.75 2.50 3.8 3.857143 3.250 1.80 stress-fmri 5
## 448 ap165 3.75 2.50 3.8 3.857143 3.250 1.80 stress-fmri 5
## 449 ap165 3.75 2.50 3.8 3.857143 3.250 1.80 stress-fmri 6
## 450 ap165 3.75 2.50 3.8 3.857143 3.250 1.80 stress-fmri 6
## 451 ap166 4.00 3.75 3.8 2.285714 3.375 1.80 stress-fmri 1
## 452 ap166 4.00 3.75 3.8 2.285714 3.375 1.80 stress-fmri 1
## 453 ap166 4.00 3.75 3.8 2.285714 3.375 1.80 stress-fmri 2
## 454 ap166 4.00 3.75 3.8 2.285714 3.375 1.80 stress-fmri 2
## 455 ap166 4.00 3.75 3.8 2.285714 3.375 1.80 stress-fmri 3
## 456 ap166 4.00 3.75 3.8 2.285714 3.375 1.80 stress-fmri 3
## 457 ap166 4.00 3.75 3.8 2.285714 3.375 1.80 stress-fmri 4
## 458 ap166 4.00 3.75 3.8 2.285714 3.375 1.80 stress-fmri 4
## 459 ap166 4.00 3.75 3.8 2.285714 3.375 1.80 stress-fmri 5
## 460 ap166 4.00 3.75 3.8 2.285714 3.375 1.80 stress-fmri 5
## 461 ap166 4.00 3.75 3.8 2.285714 3.375 1.80 stress-fmri 6
## 462 ap166 4.00 3.75 3.8 2.285714 3.375 1.80 stress-fmri 6
## 463 ap167 3.25 3.75 3.8 2.571429 4.000 1.80 stress-fmri 1
## 464 ap167 3.25 3.75 3.8 2.571429 4.000 1.80 stress-fmri 1
## 465 ap167 3.25 3.75 3.8 2.571429 4.000 1.80 stress-fmri 2
## 466 ap167 3.25 3.75 3.8 2.571429 4.000 1.80 stress-fmri 2
## 467 ap167 3.25 3.75 3.8 2.571429 4.000 1.80 stress-fmri 3
## 468 ap167 3.25 3.75 3.8 2.571429 4.000 1.80 stress-fmri 3
## 469 ap167 3.25 3.75 3.8 2.571429 4.000 1.80 stress-fmri 4
## 470 ap167 3.25 3.75 3.8 2.571429 4.000 1.80 stress-fmri 4
## 471 ap167 3.25 3.75 3.8 2.571429 4.000 1.80 stress-fmri 5
## 472 ap167 3.25 3.75 3.8 2.571429 4.000 1.80 stress-fmri 5
## 473 ap167 3.25 3.75 3.8 2.571429 4.000 1.80 stress-fmri 6
## 474 ap167 3.25 3.75 3.8 2.571429 4.000 1.80 stress-fmri 6
## 475 ap168 3.50 4.00 3.8 3.142857 2.250 2.35 stress-fmri 1
## 476 ap168 3.50 4.00 3.8 3.142857 2.250 2.35 stress-fmri 1
## 477 ap168 3.50 4.00 3.8 3.142857 2.250 2.35 stress-fmri 2
## 478 ap168 3.50 4.00 3.8 3.142857 2.250 2.35 stress-fmri 2
## 479 ap168 3.50 4.00 3.8 3.142857 2.250 2.35 stress-fmri 3
## 480 ap168 3.50 4.00 3.8 3.142857 2.250 2.35 stress-fmri 3
## 481 ap168 3.50 4.00 3.8 3.142857 2.250 2.35 stress-fmri 4
## 482 ap168 3.50 4.00 3.8 3.142857 2.250 2.35 stress-fmri 4
## 483 ap168 3.50 4.00 3.8 3.142857 2.250 2.35 stress-fmri 5
## 484 ap168 3.50 4.00 3.8 3.142857 2.250 2.35 stress-fmri 5
## 485 ap168 3.50 4.00 3.8 3.142857 2.250 2.35 stress-fmri 6
## 486 ap168 3.50 4.00 3.8 3.142857 2.250 2.35 stress-fmri 6
## 487 ap169 3.25 3.75 3.4 2.571429 3.375 2.35 stress-fmri 1
## 488 ap169 3.25 3.75 3.4 2.571429 3.375 2.35 stress-fmri 1
## 489 ap169 3.25 3.75 3.4 2.571429 3.375 2.35 stress-fmri 2
## 490 ap169 3.25 3.75 3.4 2.571429 3.375 2.35 stress-fmri 2
## 491 ap169 3.25 3.75 3.4 2.571429 3.375 2.35 stress-fmri 3
## 492 ap169 3.25 3.75 3.4 2.571429 3.375 2.35 stress-fmri 3
## 493 ap169 3.25 3.75 3.4 2.571429 3.375 2.35 stress-fmri 4
## 494 ap169 3.25 3.75 3.4 2.571429 3.375 2.35 stress-fmri 4
## 495 ap169 3.25 3.75 3.4 2.571429 3.375 2.35 stress-fmri 5
## 496 ap169 3.25 3.75 3.4 2.571429 3.375 2.35 stress-fmri 5
## 497 ap169 3.25 3.75 3.4 2.571429 3.375 2.35 stress-fmri 6
## 498 ap169 3.25 3.75 3.4 2.571429 3.375 2.35 stress-fmri 6
## 499 ap170 3.75 2.50 4.0 3.428571 3.000 2.10 stress-fmri 1
## 500 ap170 3.75 2.50 4.0 3.428571 3.000 2.10 stress-fmri 1
## 501 ap170 3.75 2.50 4.0 3.428571 3.000 2.10 stress-fmri 2
## 502 ap170 3.75 2.50 4.0 3.428571 3.000 2.10 stress-fmri 2
## 503 ap170 3.75 2.50 4.0 3.428571 3.000 2.10 stress-fmri 3
## 504 ap170 3.75 2.50 4.0 3.428571 3.000 2.10 stress-fmri 3
## 505 ap170 3.75 2.50 4.0 3.428571 3.000 2.10 stress-fmri 4
## 506 ap170 3.75 2.50 4.0 3.428571 3.000 2.10 stress-fmri 4
## 507 ap170 3.75 2.50 4.0 3.428571 3.000 2.10 stress-fmri 5
## 508 ap170 3.75 2.50 4.0 3.428571 3.000 2.10 stress-fmri 5
## 509 ap170 3.75 2.50 4.0 3.428571 3.000 2.10 stress-fmri 6
## 510 ap170 3.75 2.50 4.0 3.428571 3.000 2.10 stress-fmri 6
## 511 ap171 3.25 3.00 3.0 2.571429 3.000 1.70 stress-fmri 1
## 512 ap171 3.25 3.00 3.0 2.571429 3.000 1.70 stress-fmri 1
## 513 ap171 3.25 3.00 3.0 2.571429 3.000 1.70 stress-fmri 2
## 514 ap171 3.25 3.00 3.0 2.571429 3.000 1.70 stress-fmri 2
## 515 ap171 3.25 3.00 3.0 2.571429 3.000 1.70 stress-fmri 3
## 516 ap171 3.25 3.00 3.0 2.571429 3.000 1.70 stress-fmri 3
## 517 ap171 3.25 3.00 3.0 2.571429 3.000 1.70 stress-fmri 4
## 518 ap171 3.25 3.00 3.0 2.571429 3.000 1.70 stress-fmri 4
## 519 ap171 3.25 3.00 3.0 2.571429 3.000 1.70 stress-fmri 5
## 520 ap171 3.25 3.00 3.0 2.571429 3.000 1.70 stress-fmri 5
## 521 ap171 3.25 3.00 3.0 2.571429 3.000 1.70 stress-fmri 6
## 522 ap171 3.25 3.00 3.0 2.571429 3.000 1.70 stress-fmri 6
## 523 ap172 3.00 3.00 3.2 3.571429 3.000 2.70 stress-fmri 1
## 524 ap172 3.00 3.00 3.2 3.571429 3.000 2.70 stress-fmri 1
## 525 ap172 3.00 3.00 3.2 3.571429 3.000 2.70 stress-fmri 2
## 526 ap172 3.00 3.00 3.2 3.571429 3.000 2.70 stress-fmri 2
## 527 ap172 3.00 3.00 3.2 3.571429 3.000 2.70 stress-fmri 3
## 528 ap172 3.00 3.00 3.2 3.571429 3.000 2.70 stress-fmri 3
## 529 ap172 3.00 3.00 3.2 3.571429 3.000 2.70 stress-fmri 4
## 530 ap172 3.00 3.00 3.2 3.571429 3.000 2.70 stress-fmri 4
## 531 ap172 3.00 3.00 3.2 3.571429 3.000 2.70 stress-fmri 5
## 532 ap172 3.00 3.00 3.2 3.571429 3.000 2.70 stress-fmri 5
## 533 ap172 3.00 3.00 3.2 3.571429 3.000 2.70 stress-fmri 6
## 534 ap172 3.00 3.00 3.2 3.571429 3.000 2.70 stress-fmri 6
## 535 ap173 3.00 3.75 3.6 2.714286 3.125 2.40 stress-fmri 1
## 536 ap173 3.00 3.75 3.6 2.714286 3.125 2.40 stress-fmri 1
## 537 ap173 3.00 3.75 3.6 2.714286 3.125 2.40 stress-fmri 2
## 538 ap173 3.00 3.75 3.6 2.714286 3.125 2.40 stress-fmri 2
## 539 ap173 3.00 3.75 3.6 2.714286 3.125 2.40 stress-fmri 3
## 540 ap173 3.00 3.75 3.6 2.714286 3.125 2.40 stress-fmri 3
## 541 ap173 3.00 3.75 3.6 2.714286 3.125 2.40 stress-fmri 4
## 542 ap173 3.00 3.75 3.6 2.714286 3.125 2.40 stress-fmri 4
## 543 ap173 3.00 3.75 3.6 2.714286 3.125 2.40 stress-fmri 5
## 544 ap173 3.00 3.75 3.6 2.714286 3.125 2.40 stress-fmri 5
## 545 ap173 3.00 3.75 3.6 2.714286 3.125 2.40 stress-fmri 6
## 546 ap173 3.00 3.75 3.6 2.714286 3.125 2.40 stress-fmri 6
## 547 ap174 3.25 3.25 3.6 3.000000 3.000 1.65 stress-fmri 1
## 548 ap174 3.25 3.25 3.6 3.000000 3.000 1.65 stress-fmri 1
## 549 ap174 3.25 3.25 3.6 3.000000 3.000 1.65 stress-fmri 2
## 550 ap174 3.25 3.25 3.6 3.000000 3.000 1.65 stress-fmri 2
## 551 ap174 3.25 3.25 3.6 3.000000 3.000 1.65 stress-fmri 3
## 552 ap174 3.25 3.25 3.6 3.000000 3.000 1.65 stress-fmri 3
## 553 ap174 3.25 3.25 3.6 3.000000 3.000 1.65 stress-fmri 4
## 554 ap174 3.25 3.25 3.6 3.000000 3.000 1.65 stress-fmri 4
## 555 ap174 3.25 3.25 3.6 3.000000 3.000 1.65 stress-fmri 5
## 556 ap174 3.25 3.25 3.6 3.000000 3.000 1.65 stress-fmri 5
## 557 ap174 3.25 3.25 3.6 3.000000 3.000 1.65 stress-fmri 6
## 558 ap174 3.25 3.25 3.6 3.000000 3.000 1.65 stress-fmri 6
## shockCond subScaleName adjustedRating respRT remove good_math
## 1 safe Negative 1.5 3.918950 NA 5
## 2 safe Positive 2.5 3.094750 NA 5
## 3 threat Negative 2.5 1.321420 NA 5
## 4 threat Positive 2.5 3.323400 NA 5
## 5 threat Negative 2.0 3.216950 NA 5
## 6 threat Positive 2.5 1.801150 NA 5
## 7 safe Negative 2.5 3.536500 NA 5
## 8 safe Positive 2.0 2.143600 NA 5
## 9 threat Negative 2.0 2.578950 NA 5
## 10 threat Positive 2.0 1.241300 NA 5
## 11 safe Negative 1.5 2.118700 NA 5
## 12 safe Positive 3.5 4.093500 NA 5
## 13 threat Negative 1.5 0.807115 NA 5
## 14 threat Positive 4.0 3.464300 NA 5
## 15 safe Negative 2.0 1.767500 NA 5
## 16 safe Positive 3.0 1.723800 NA 5
## 17 threat Negative 3.0 1.445750 NA 5
## 18 threat Positive 4.0 1.253100 NA 5
## 19 safe Negative 2.0 1.391000 NA 5
## 20 safe Positive 3.0 1.267600 NA 5
## 21 threat Negative 2.0 1.250150 NA 5
## 22 threat Positive 3.0 1.347050 NA 5
## 23 threat Negative 3.5 2.681050 NA 5
## 24 threat Positive 3.5 2.823550 NA 5
## 25 safe Negative 3.0 2.449450 NA 5
## 26 safe Positive 3.5 1.776350 NA 5
## 27 threat Negative 4.0 3.506800 NA 5
## 28 threat Positive 3.5 1.944250 NA 5
## 29 safe Negative 3.0 2.145750 NA 5
## 30 safe Positive 3.0 0.875965 NA 5
## 31 threat Negative 3.0 2.060950 NA 5
## 32 threat Positive 4.0 2.341650 NA 5
## 33 safe Negative 3.0 2.196400 NA 5
## 34 safe Positive 3.5 1.824440 NA 5
## 35 safe Negative 1.5 8.743350 NA 3
## 36 safe Positive 3.5 6.341850 NA 3
## 37 threat Negative 2.0 5.823150 NA 3
## 38 threat Positive 2.0 6.674950 NA 3
## 39 safe Negative 1.5 9.728050 NA 3
## 40 safe Positive 3.0 3.001800 NA 3
## 41 threat Negative 1.0 4.531200 NA 3
## 42 threat Positive 2.5 4.483900 NA 3
## 43 safe Negative 1.0 4.725650 NA 3
## 44 safe Positive 2.5 3.326050 NA 3
## 45 threat Negative 1.0 6.962550 NA 3
## 46 threat Positive 3.0 2.725000 NA 3
## 47 threat Negative 2.5 3.453650 NA 5
## 48 threat Positive 3.0 4.114300 NA 5
## 49 safe Negative 1.5 5.059200 NA 5
## 50 safe Positive 3.0 3.957250 NA 5
## 51 threat Negative 1.5 4.733850 NA 5
## 52 threat Positive 3.0 1.391300 NA 5
## 53 safe Negative 3.0 5.071350 NA 5
## 54 safe Positive 2.5 3.081150 NA 5
## 55 threat Negative 2.0 3.251150 NA 5
## 56 threat Positive 2.5 1.546700 NA 5
## 57 safe Negative 1.5 3.425900 NA 5
## 58 safe Positive 4.0 3.543900 NA 5
## 59 safe Negative 3.0 6.441000 NA 5
## 60 safe Positive 2.5 6.178400 NA 5
## 61 threat Negative 3.0 3.051600 NA 5
## 62 threat Positive 2.0 2.469450 NA 5
## 63 safe Negative 3.5 3.888900 NA 5
## 64 safe Positive 3.0 3.871800 NA 5
## 65 threat Negative 1.5 4.928250 NA 5
## 66 threat Positive 3.0 6.611550 NA 5
## 67 safe Negative 2.5 3.338250 NA 5
## 68 safe Positive 3.0 3.218850 NA 5
## 69 threat Negative 3.0 5.120500 NA 5
## 70 threat Positive 3.0 3.612900 NA 5
## 71 safe Negative 3.0 1.046100 NA 6
## 72 safe Positive 3.5 2.659450 NA 6
## 73 threat Negative 3.0 2.885350 NA 6
## 74 threat Positive 3.0 1.562500 NA 6
## 75 safe Negative 3.0 2.210850 NA 6
## 76 safe Positive 2.5 1.611050 NA 6
## 77 threat Negative 3.0 1.616000 NA 6
## 78 threat Positive 3.5 2.023550 NA 6
## 79 safe Negative 3.5 2.451100 NA 6
## 80 safe Positive 3.0 2.224150 NA 6
## 81 safe Negative 3.0 2.537650 NA 5
## 82 safe Positive 2.0 4.504750 NA 5
## 83 threat Negative 2.0 2.506650 NA 5
## 84 threat Positive 2.0 2.327450 NA 5
## 85 safe Negative 2.0 2.921550 NA 5
## 86 safe Positive 3.5 4.302450 NA 5
## 87 threat Negative 2.0 1.995700 NA 5
## 88 threat Positive 3.5 2.276200 NA 5
## 89 safe Negative 2.0 1.949950 NA 5
## 90 safe Positive 3.0 2.172500 NA 5
## 91 threat Negative 2.0 2.130400 NA 5
## 92 threat Positive 3.0 2.791050 NA 5
## 93 threat Negative 2.5 7.745350 NA 5
## 94 threat Positive 2.5 5.918600 NA 5
## 95 safe Negative 2.0 4.411250 NA 5
## 96 safe Positive 2.0 6.871600 NA 5
## 97 threat Negative 2.0 8.558650 NA 5
## 98 threat Positive 2.5 6.090100 NA 5
## 99 safe Negative 1.0 5.983050 NA 5
## 100 safe Positive 2.0 8.936500 NA 5
## 101 threat Negative 2.5 7.954750 NA 5
## 102 threat Positive 1.5 3.126250 NA 5
## 103 safe Negative 2.0 2.191700 NA 5
## 104 safe Positive 1.5 1.876700 NA 5
## 105 safe Negative 2.5 8.067650 NA 4
## 106 safe Positive 3.0 7.094100 NA 4
## 107 threat Negative 2.5 11.697950 NA 4
## 108 threat Positive 3.0 9.694950 NA 4
## 109 safe Negative 2.5 7.725550 NA 4
## 110 safe Positive 2.0 9.076350 NA 4
## 111 threat Negative 2.5 7.280750 NA 4
## 112 threat Positive 3.0 3.631600 NA 4
## 113 safe Negative 3.0 4.997900 NA 4
## 114 safe Positive 3.0 3.686600 NA 4
## 115 threat Negative 3.0 2.730250 NA 4
## 116 threat Positive 3.0 1.966950 NA 4
## 117 threat Negative 2.5 6.438200 NA 5
## 118 threat Positive 2.5 3.877850 NA 5
## 119 safe Negative 1.0 1.623705 NA 5
## 120 safe Positive 2.5 3.566600 NA 5
## 121 threat Negative 1.0 2.284100 NA 5
## 122 threat Positive 2.5 2.016300 NA 5
## 123 safe Negative 1.0 1.625550 NA 5
## 124 safe Positive 2.5 1.656700 NA 5
## 125 threat Negative 1.0 1.230250 NA 5
## 126 threat Positive 2.5 1.464650 NA 5
## 127 safe Negative 1.5 2.961700 NA 5
## 128 safe Positive 2.5 1.315420 NA 5
## 129 safe Negative 2.5 4.451950 NA 6
## 130 safe Positive 4.0 4.038450 NA 6
## 131 threat Negative 2.5 3.709050 NA 6
## 132 threat Positive 3.5 1.777100 NA 6
## 133 safe Negative 2.0 2.843000 NA 6
## 134 safe Positive 3.0 1.965300 NA 6
## 135 threat Negative 2.0 3.482050 NA 6
## 136 threat Positive 3.0 1.825750 NA 6
## 137 safe Negative 2.5 1.761000 NA 6
## 138 safe Positive 3.0 1.508650 NA 6
## 139 threat Negative 2.0 2.339600 NA 6
## 140 threat Positive 3.5 2.823450 NA 6
## 141 safe Negative 3.0 4.775950 NA 6
## 142 safe Positive 3.0 4.980850 NA 6
## 143 threat Negative 3.0 3.546950 NA 6
## 144 threat Positive 3.0 2.112050 NA 6
## 145 safe Negative 3.0 3.321300 NA 6
## 146 safe Positive 3.0 2.288150 NA 6
## 147 threat Negative 3.5 1.964300 NA 6
## 148 threat Positive 3.0 1.609450 NA 6
## 149 safe Negative 4.0 2.749500 NA 6
## 150 safe Positive 3.0 1.653300 NA 6
## 151 threat Negative 4.0 2.391680 NA 6
## 152 threat Positive 3.0 1.759950 NA 6
## 153 threat Negative 2.0 4.570650 NA 5
## 154 threat Positive 2.5 3.435850 NA 5
## 155 safe Negative 2.0 8.972800 NA 5
## 156 safe Positive 3.5 2.130100 NA 5
## 157 threat Negative 1.5 4.297000 NA 5
## 158 threat Positive 3.5 4.868100 NA 5
## 159 safe Negative 2.5 3.192150 NA 5
## 160 safe Positive 2.5 2.034500 NA 5
## 161 threat Negative 1.5 5.353100 NA 5
## 162 threat Positive 3.0 1.814950 NA 5
## 163 safe Negative 1.5 1.931270 NA 5
## 164 safe Positive 3.5 3.524450 NA 5
## 165 safe Negative 2.5 4.614450 NA 7
## 166 safe Positive 4.0 4.390550 NA 7
## 167 threat Negative 3.0 3.242950 NA 7
## 168 threat Positive 4.0 2.399700 NA 7
## 169 safe Negative 2.5 2.849900 NA 7
## 170 safe Positive 4.0 2.420950 NA 7
## 171 threat Negative 2.0 2.945550 NA 7
## 172 threat Positive 3.5 3.029200 NA 7
## 173 safe Negative 2.5 2.742100 NA 7
## 174 safe Positive 3.0 2.430050 NA 7
## 175 threat Negative 2.0 2.214750 NA 7
## 176 threat Positive 3.0 2.059400 NA 7
## 177 threat Negative 3.5 6.365550 NA 5
## 178 threat Positive 2.5 13.503700 NA 5
## 179 safe Negative 1.0 3.155250 NA 5
## 180 safe Positive 3.0 2.049150 NA 5
## 181 threat Negative 1.5 2.850200 NA 5
## 182 threat Positive 3.0 2.181500 NA 5
## 183 safe Negative 1.0 1.703100 NA 5
## 184 safe Positive 2.5 3.167800 NA 5
## 185 threat Negative 1.0 1.420850 NA 5
## 186 threat Positive 3.0 1.394700 NA 5
## 187 safe Negative 1.0 1.723700 NA 5
## 188 safe Positive 3.5 2.381900 NA 5
## 189 safe Negative 1.0 3.618400 NA 7
## 190 safe Positive 1.0 4.599800 NA 7
## 191 threat Negative 1.5 1.565330 NA 7
## 192 threat Positive 2.0 13.877850 NA 7
## 193 safe Negative 1.5 2.307250 NA 7
## 194 safe Positive 3.0 7.615950 NA 7
## 195 threat Negative 1.0 4.617750 NA 7
## 196 threat Positive 3.0 4.375300 NA 7
## 197 safe Negative 1.0 5.358250 NA 7
## 198 safe Positive 3.5 4.096100 NA 7
## 199 threat Negative 1.0 6.032200 NA 7
## 200 threat Positive 3.0 3.535150 NA 7
## 201 threat Negative 3.5 3.500650 NA 7
## 202 threat Positive 3.0 2.727900 NA 7
## 203 safe Negative 3.0 2.529550 NA 7
## 204 safe Positive 3.0 1.686250 NA 7
## 205 threat Negative 3.0 1.613400 NA 7
## 206 threat Positive 3.5 1.340600 NA 7
## 207 safe Negative 2.5 2.674950 NA 7
## 208 safe Positive 2.5 5.663700 NA 7
## 209 threat Negative 2.5 2.337050 NA 7
## 210 threat Positive 3.0 3.143050 NA 7
## 211 safe Negative 2.5 2.833400 NA 7
## 212 safe Positive 2.5 1.445150 NA 7
## 213 safe Negative 2.0 7.754150 NA 4
## 214 safe Positive 4.0 10.445500 NA 4
## 215 threat Negative 2.0 3.940050 NA 4
## 216 threat Positive 3.0 5.237600 NA 4
## 217 safe Negative 2.0 2.517400 NA 4
## 218 safe Positive 3.0 2.740300 NA 4
## 219 threat Negative 3.0 3.822700 NA 4
## 220 threat Positive 3.0 2.049850 NA 4
## 221 safe Negative 2.0 3.088700 NA 4
## 222 safe Positive 3.0 2.009500 NA 4
## 223 threat Negative 2.0 1.112775 NA 4
## 224 threat Positive 3.0 1.281570 NA 4
## 225 threat Negative 2.0 5.074850 NA 5
## 226 threat Positive 2.0 4.869750 NA 5
## 227 safe Negative 1.0 3.751950 NA 5
## 228 safe Positive 3.0 3.844000 NA 5
## 229 threat Negative 1.5 3.549400 NA 5
## 230 threat Positive 3.0 2.440100 NA 5
## 231 safe Negative 1.0 2.353350 NA 5
## 232 safe Positive 2.5 2.040700 NA 5
## 233 threat Negative 1.0 1.471735 NA 5
## 234 threat Positive 3.0 1.894550 NA 5
## 235 safe Negative 1.0 1.141320 NA 5
## 236 safe Positive 3.0 1.144955 NA 5
## 237 safe Negative 3.5 6.775750 NA 7
## 238 safe Positive 2.5 3.290700 NA 7
## 239 threat Negative 2.5 7.501350 NA 7
## 240 threat Positive 3.0 2.766850 NA 7
## 241 safe Negative 2.5 2.011250 NA 7
## 242 safe Positive 3.5 1.631300 NA 7
## 243 threat Negative 2.0 1.531300 NA 7
## 244 threat Positive 4.0 1.816275 NA 7
## 245 safe Negative 2.5 1.292750 NA 7
## 246 safe Positive 3.5 0.880075 NA 7
## 247 threat Negative 4.0 4.363300 NA 7
## 248 threat Positive 3.5 3.630400 NA 7
## 249 threat Negative 1.5 5.391600 NA 6
## 250 threat Positive 2.5 4.621200 NA 6
## 251 safe Negative 1.0 3.308750 NA 6
## 252 safe Positive 3.0 2.202700 NA 6
## 253 threat Negative 1.0 1.705000 NA 6
## 254 threat Positive 3.0 2.211800 NA 6
## 255 safe Negative 1.0 1.853100 NA 6
## 256 safe Positive 3.0 2.124000 NA 6
## 257 threat Negative 2.0 0.989860 NA 6
## 258 threat Positive 3.5 1.302715 NA 6
## 259 safe Negative 2.0 2.413400 NA 6
## 260 safe Positive 3.0 2.049000 NA 6
## 261 threat Negative 2.5 5.532750 NA 5
## 262 threat Positive 2.0 3.866150 NA 5
## 263 safe Negative 3.5 2.040250 NA 5
## 264 safe Positive 2.5 1.474750 NA 5
## 265 threat Negative 3.0 2.925900 NA 5
## 266 threat Positive 2.5 2.592800 NA 5
## 267 safe Negative 2.5 1.945200 NA 5
## 268 safe Positive 1.5 1.914350 NA 5
## 269 threat Negative 3.5 2.487200 NA 5
## 270 threat Positive 2.5 1.556250 NA 5
## 271 safe Negative 3.0 3.633900 NA 5
## 272 safe Positive 3.0 2.901900 NA 5
## 273 safe Negative 3.0 3.294350 NA 3
## 274 safe Positive 1.5 6.465950 NA 3
## 275 threat Negative 2.0 1.847795 NA 3
## 276 threat Positive 2.0 3.225050 NA 3
## 277 safe Negative 2.5 1.688465 NA 3
## 278 safe Positive 1.5 3.435350 NA 3
## 279 threat Negative 2.5 1.347465 NA 3
## 280 threat Positive 2.0 2.375750 NA 3
## 281 safe Negative 3.0 2.757850 NA 3
## 282 safe Positive 1.5 2.532150 NA 3
## 283 threat Negative 3.5 4.355450 NA 3
## 284 threat Positive 2.5 3.057250 NA 3
## 285 threat Negative 3.5 2.707550 NA 5
## 286 threat Positive 3.0 6.605600 NA 5
## 287 safe Negative 1.0 3.006300 NA 5
## 288 safe Positive 4.0 4.794750 NA 5
## 289 threat Negative 2.5 2.715800 NA 5
## 290 threat Positive 2.0 3.849200 NA 5
## 291 safe Negative 1.5 3.401100 NA 5
## 292 safe Positive 3.0 3.974100 NA 5
## 293 threat Negative 3.0 5.123400 NA 5
## 294 threat Positive 2.0 3.027900 NA 5
## 295 safe Negative 1.0 2.780665 NA 5
## 296 safe Positive 3.5 3.843650 NA 5
## 297 safe Negative 2.5 6.276050 NA 5
## 298 safe Positive 4.0 12.471850 NA 5
## 299 threat Negative 1.0 7.571150 NA 5
## 300 threat Positive 3.0 5.740750 NA 5
## 301 safe Negative 1.5 6.940200 NA 5
## 302 safe Positive 3.5 6.047250 NA 5
## 303 threat Negative 2.0 4.426100 NA 5
## 304 threat Positive 2.0 3.312100 NA 5
## 305 safe Negative 1.0 3.691650 NA 5
## 306 safe Positive 3.0 3.980300 NA 5
## 307 threat Negative 1.5 4.031235 NA 5
## 308 threat Positive 3.0 5.515450 NA 5
## 309 threat Negative 2.5 5.728200 NA 6
## 310 threat Positive 2.5 3.540250 NA 6
## 311 safe Negative 1.5 2.580150 NA 6
## 312 safe Positive 2.0 12.837450 NA 6
## 313 threat Negative 3.0 4.092800 NA 6
## 314 threat Positive 2.0 1.982350 NA 6
## 315 safe Negative 2.0 1.797000 NA 6
## 316 safe Positive 2.5 1.766300 NA 6
## 317 threat Negative 4.0 1.923850 NA 6
## 318 threat Positive 2.5 2.658450 NA 6
## 319 safe Negative 1.5 3.449850 NA 6
## 320 safe Positive 2.0 1.912600 NA 6
## 321 safe Negative 1.5 9.720100 NA 5
## 322 safe Positive 3.0 7.837950 NA 5
## 323 threat Negative 1.0 5.158150 NA 5
## 324 threat Positive 3.0 3.729750 NA 5
## 325 safe Negative 1.0 2.335800 NA 5
## 326 safe Positive 3.0 2.865150 NA 5
## 327 threat Negative 1.0 2.226850 NA 5
## 328 threat Positive 3.0 2.795600 NA 5
## 329 safe Negative 1.0 5.546300 NA 5
## 330 safe Positive 3.0 1.389680 NA 5
## 331 threat Negative 2.5 5.101800 NA 4
## 332 threat Positive 3.0 6.448600 NA 4
## 333 safe Negative 1.5 4.316350 NA 4
## 334 safe Positive 3.0 3.676350 NA 4
## 335 threat Negative 1.0 2.326000 NA 4
## 336 threat Positive 3.0 2.073400 NA 4
## 337 safe Negative 1.0 1.957850 NA 4
## 338 safe Positive 3.0 3.407250 NA 4
## 339 threat Negative 1.0 1.775900 NA 4
## 340 threat Positive 3.0 1.437300 NA 4
## 341 safe Negative 1.5 1.497250 NA 4
## 342 safe Positive 3.0 1.425250 NA 4
## 343 safe Negative 3.0 5.403750 NA 5
## 344 safe Positive 2.5 5.004150 NA 5
## 345 threat Negative 2.5 3.820300 NA 5
## 346 threat Positive 2.5 3.120200 NA 5
## 347 safe Negative 2.5 3.058800 NA 5
## 348 safe Positive 2.5 2.986500 NA 5
## 349 threat Negative 1.5 3.118300 NA 5
## 350 threat Positive 1.0 1.865600 NA 5
## 351 safe Negative 1.0 4.196900 NA 5
## 352 safe Positive 2.5 2.739550 NA 5
## 353 threat Negative 1.0 1.019960 NA 5
## 354 threat Positive 1.5 3.337300 NA 5
## 355 threat Negative 1.0 3.387500 NA 5
## 356 threat Positive 3.0 3.797850 NA 5
## 357 safe Negative 1.0 3.813500 NA 5
## 358 safe Positive 3.0 3.844150 NA 5
## 359 threat Negative 1.0 2.801600 NA 5
## 360 threat Positive 3.0 4.065750 NA 5
## 361 safe Negative 1.0 1.997200 NA 5
## 362 safe Positive 3.0 1.897050 NA 5
## 363 threat Negative 1.0 1.809550 NA 5
## 364 threat Positive 3.0 3.235950 NA 5
## 365 safe Negative 1.0 1.135350 NA 5
## 366 safe Positive 3.0 2.069550 NA 5
## 367 safe Negative 2.5 6.080500 NA 4
## 368 safe Positive 2.5 6.194700 NA 4
## 369 threat Negative 2.0 1.465265 NA 4
## 370 threat Positive 2.0 5.588000 NA 4
## 371 safe Negative 1.5 1.905795 NA 4
## 372 safe Positive 2.0 5.451300 NA 4
## 373 threat Negative 2.0 1.717335 NA 4
## 374 threat Positive 1.5 4.980650 NA 4
## 375 safe Negative 1.5 2.166015 NA 4
## 376 safe Positive 2.5 6.800450 NA 4
## 377 threat Negative 2.0 4.431900 NA 4
## 378 threat Positive 2.5 3.026750 NA 4
## 379 threat Negative 2.5 4.372300 NA 4
## 380 threat Positive 3.0 2.586350 NA 4
## 381 safe Negative 1.0 2.174600 NA 4
## 382 safe Positive 1.5 1.914500 NA 4
## 383 threat Negative 1.5 4.051350 NA 4
## 384 threat Positive 2.0 1.934750 NA 4
## 385 safe Negative 1.0 2.010800 NA 4
## 386 safe Positive 1.5 1.531150 NA 4
## 387 threat Negative 3.0 2.622700 NA 4
## 388 threat Positive 2.0 2.156650 NA 4
## 389 safe Negative 1.0 3.167550 NA 4
## 390 safe Positive 3.0 4.438400 NA 4
## 391 safe Negative 2.5 3.137000 NA 5
## 392 safe Positive 4.0 5.092300 NA 5
## 393 threat Negative 2.5 1.479695 NA 5
## 394 threat Positive 4.0 6.619200 NA 5
## 395 safe Negative 2.5 3.480250 NA 5
## 396 safe Positive 3.5 9.378650 NA 5
## 397 threat Negative 3.0 1.661770 NA 5
## 398 threat Positive 3.5 6.118400 NA 5
## 399 safe Negative 3.5 2.621800 NA 5
## 400 safe Positive 3.0 7.354300 NA 5
## 401 threat Negative 3.5 3.504100 NA 5
## 402 threat Positive 3.5 5.360300 NA 5
## 403 threat Negative 1.5 5.926300 NA 7
## 404 threat Positive 1.5 1.510800 NA 7
## 405 safe Negative 2.5 3.857150 NA 7
## 406 safe Positive 3.0 1.545050 NA 7
## 407 threat Negative 3.5 2.923650 NA 7
## 408 threat Positive 2.5 2.839850 NA 7
## 409 safe Negative 2.0 2.401150 NA 7
## 410 safe Positive 2.0 2.146600 NA 7
## 411 threat Negative 3.5 2.476800 NA 7
## 412 threat Positive 1.5 1.457300 NA 7
## 413 safe Negative 2.0 0.401840 NA 7
## 414 safe Positive 2.0 0.251545 NA 7
## 415 safe Negative 2.0 2.707450 NA 6
## 416 safe Positive 2.5 3.262900 NA 6
## 417 threat Negative 3.0 3.044850 NA 6
## 418 threat Positive 1.0 2.536400 NA 6
## 419 safe Negative 2.0 3.946550 NA 6
## 420 safe Positive 2.5 2.067650 NA 6
## 421 threat Negative 2.5 2.584950 NA 6
## 422 threat Positive 1.5 1.938500 NA 6
## 423 safe Negative 1.5 1.879500 NA 6
## 424 safe Positive 2.5 1.899550 NA 6
## 425 threat Negative 2.0 1.894050 NA 6
## 426 threat Positive 2.0 1.726975 NA 6
## 427 threat Negative 4.0 6.772800 NA 6
## 428 threat Positive 3.0 2.830300 NA 6
## 429 safe Negative 3.5 3.308300 NA 6
## 430 safe Positive 2.5 3.009900 NA 6
## 431 threat Negative 3.0 2.509350 NA 6
## 432 threat Positive 3.5 2.148300 NA 6
## 433 safe Negative 3.0 3.695100 NA 6
## 434 safe Positive 1.5 2.710700 NA 6
## 435 threat Negative 2.0 2.808900 NA 6
## 436 threat Positive 2.0 1.994950 NA 6
## 437 safe Negative 2.0 1.747900 NA 6
## 438 safe Positive 3.5 1.681050 NA 6
## 439 safe Negative 2.0 3.173450 NA 5
## 440 safe Positive 2.5 3.591850 NA 5
## 441 threat Negative 3.0 4.096300 NA 5
## 442 threat Positive 1.5 3.704050 NA 5
## 443 safe Negative 1.5 4.757450 NA 5
## 444 safe Positive 3.0 3.709700 NA 5
## 445 threat Negative 3.0 2.836350 NA 5
## 446 threat Positive 1.5 2.128850 NA 5
## 447 safe Negative 2.0 5.122050 NA 5
## 448 safe Positive 3.5 2.433700 NA 5
## 449 threat Negative 3.0 1.849350 NA 5
## 450 threat Positive 1.5 1.860250 NA 5
## 451 threat Negative 2.5 4.443700 NA 5
## 452 threat Positive 3.5 5.909950 NA 5
## 453 safe Negative 1.0 4.852800 NA 5
## 454 safe Positive 3.0 6.099500 NA 5
## 455 threat Negative 1.0 2.979550 NA 5
## 456 threat Positive 3.5 4.594100 NA 5
## 457 safe Negative 1.0 2.565450 NA 5
## 458 safe Positive 3.0 3.130750 NA 5
## 459 threat Negative 3.0 5.915600 NA 5
## 460 threat Positive 2.5 3.092050 NA 5
## 461 safe Negative 3.5 4.059300 NA 5
## 462 safe Positive 2.5 1.950200 NA 5
## 463 safe Negative 2.5 4.538450 NA 6
## 464 safe Positive 2.0 3.680850 NA 6
## 465 threat Negative 4.0 2.056715 NA 6
## 466 threat Positive 2.5 1.418650 NA 6
## 467 safe Negative 2.0 1.469115 NA 6
## 468 safe Positive 2.0 1.314505 NA 6
## 469 threat Negative 3.5 1.331700 NA 6
## 470 threat Positive 2.5 4.057300 NA 6
## 471 safe Negative 2.0 1.417750 NA 6
## 472 safe Positive 2.0 1.130385 NA 6
## 473 threat Negative 4.0 1.586105 NA 6
## 474 threat Positive 1.0 1.005095 NA 6
## 475 threat Negative 4.0 11.189150 NA 6
## 476 threat Positive 1.0 6.151150 NA 6
## 477 safe Negative 4.0 3.024000 NA 6
## 478 safe Positive 2.0 2.991100 NA 6
## 479 threat Negative 4.0 4.473050 NA 6
## 480 threat Positive 2.5 3.710250 NA 6
## 481 safe Negative 3.5 3.769450 NA 6
## 482 safe Positive 1.5 2.396750 NA 6
## 483 threat Negative 4.0 2.195100 NA 6
## 484 threat Positive 2.5 1.802050 NA 6
## 485 safe Negative 4.0 1.995300 NA 6
## 486 safe Positive 2.5 1.067620 NA 6
## 487 safe Negative 3.5 7.324800 NA 6
## 488 safe Positive 3.0 4.528900 NA 6
## 489 threat Negative 3.5 2.565300 NA 6
## 490 threat Positive 3.0 7.454200 NA 6
## 491 safe Negative 3.0 2.765950 NA 6
## 492 safe Positive 3.5 1.512500 NA 6
## 493 threat Negative 4.0 1.782800 NA 6
## 494 threat Positive 3.0 2.827750 NA 6
## 495 safe Negative 3.0 2.024150 NA 6
## 496 safe Positive 3.5 2.553200 NA 6
## 497 threat Negative 4.0 1.842550 NA 6
## 498 threat Positive 3.0 2.002750 NA 6
## 499 threat Negative 3.0 5.640050 NA 4
## 500 threat Positive 2.0 6.743300 NA 4
## 501 safe Negative 1.0 1.706855 NA 4
## 502 safe Positive 3.0 5.275250 NA 4
## 503 threat Negative 2.5 4.313250 NA 4
## 504 threat Positive 2.5 4.630450 NA 4
## 505 safe Negative 1.0 3.020900 NA 4
## 506 safe Positive 3.0 3.943800 NA 4
## 507 threat Negative 1.5 3.284250 NA 4
## 508 threat Positive 3.0 3.551700 NA 4
## 509 safe Negative 1.0 2.993650 NA 4
## 510 safe Positive 2.5 2.521400 NA 4
## 511 safe Negative 2.5 5.912600 NA 4
## 512 safe Positive 3.5 4.860800 NA 4
## 513 threat Negative 2.0 2.192030 NA 4
## 514 threat Positive 3.0 4.345950 NA 4
## 515 safe Negative 3.0 5.923950 NA 4
## 516 safe Positive 3.0 2.529100 NA 4
## 517 threat Negative 4.0 4.487650 NA 4
## 518 threat Positive 3.0 2.560900 NA 4
## 519 safe Negative 3.0 2.826050 NA 4
## 520 safe Positive 3.0 2.150000 NA 4
## 521 threat Negative 4.0 2.573450 NA 4
## 522 threat Positive 3.0 2.357750 NA 4
## 523 threat Negative 3.5 7.842950 NA 4
## 524 threat Positive 3.0 7.939200 NA 4
## 525 safe Negative 1.0 4.149400 NA 4
## 526 safe Positive 3.0 5.069250 NA 4
## 527 threat Negative 2.0 4.477400 NA 4
## 528 threat Positive 2.0 4.868150 NA 4
## 529 safe Negative 1.0 4.347700 NA 4
## 530 safe Positive 2.0 5.212800 NA 4
## 531 threat Negative 2.0 3.220200 NA 4
## 532 threat Positive 2.0 4.365500 NA 4
## 533 safe Negative 1.0 3.090650 NA 4
## 534 safe Positive 4.0 4.334200 NA 4
## 535 safe Negative 2.0 6.751100 NA 4
## 536 safe Positive 3.0 3.623000 NA 4
## 537 threat Negative 3.0 3.844050 NA 4
## 538 threat Positive 2.0 3.426050 NA 4
## 539 safe Negative 2.5 2.565150 NA 4
## 540 safe Positive 2.0 3.694400 NA 4
## 541 threat Negative 4.0 2.217350 NA 4
## 542 threat Positive 1.5 3.204250 NA 4
## 543 safe Negative 1.5 2.830550 NA 4
## 544 safe Positive 2.5 2.670700 NA 4
## 545 threat Negative 4.0 3.205400 NA 4
## 546 threat Positive 1.5 1.845200 NA 4
## 547 threat Negative 1.5 6.009350 NA 6
## 548 threat Positive 2.5 4.984150 NA 6
## 549 safe Negative 2.0 3.401700 NA 6
## 550 safe Positive 3.0 4.077650 NA 6
## 551 threat Negative 3.0 2.108650 NA 6
## 552 threat Positive 2.5 5.229350 NA 6
## 553 safe Negative 1.0 3.622550 NA 6
## 554 safe Positive 3.0 1.883450 NA 6
## 555 threat Negative 1.0 4.023900 NA 6
## 556 threat Positive 3.5 2.075400 NA 6
## 557 safe Negative 1.0 3.018900 NA 6
## 558 safe Positive 3.0 1.578350 NA 6
## important_math anxious happy safe stressed life_stress sleep
## 1 6 3 2 3 3 4 4.5000
## 2 6 3 2 3 3 4 4.5000
## 3 6 3 2 3 3 4 4.5000
## 4 6 3 2 3 3 4 4.5000
## 5 6 3 2 3 3 4 4.5000
## 6 6 3 2 3 3 4 4.5000
## 7 6 3 2 3 3 4 4.5000
## 8 6 3 2 3 3 4 4.5000
## 9 6 3 2 3 3 4 4.5000
## 10 6 3 2 3 3 4 4.5000
## 11 6 2 5 6 2 2 8.5000
## 12 6 2 5 6 2 2 8.5000
## 13 6 2 5 6 2 2 8.5000
## 14 6 2 5 6 2 2 8.5000
## 15 6 2 5 6 2 2 8.5000
## 16 6 2 5 6 2 2 8.5000
## 17 6 2 5 6 2 2 8.5000
## 18 6 2 5 6 2 2 8.5000
## 19 6 2 5 6 2 2 8.5000
## 20 6 2 5 6 2 2 8.5000
## 21 6 2 5 6 2 2 8.5000
## 22 6 2 5 6 2 2 8.5000
## 23 7 4 2 5 5 4 9.0000
## 24 7 4 2 5 5 4 9.0000
## 25 7 4 2 5 5 4 9.0000
## 26 7 4 2 5 5 4 9.0000
## 27 7 4 2 5 5 4 9.0000
## 28 7 4 2 5 5 4 9.0000
## 29 7 4 2 5 5 4 9.0000
## 30 7 4 2 5 5 4 9.0000
## 31 7 4 2 5 5 4 9.0000
## 32 7 4 2 5 5 4 9.0000
## 33 7 4 2 5 5 4 9.0000
## 34 7 4 2 5 5 4 9.0000
## 35 2 2 3 5 2 2 6.5000
## 36 2 2 3 5 2 2 6.5000
## 37 2 2 3 5 2 2 6.5000
## 38 2 2 3 5 2 2 6.5000
## 39 2 2 3 5 2 2 6.5000
## 40 2 2 3 5 2 2 6.5000
## 41 2 2 3 5 2 2 6.5000
## 42 2 2 3 5 2 2 6.5000
## 43 2 2 3 5 2 2 6.5000
## 44 2 2 3 5 2 2 6.5000
## 45 2 2 3 5 2 2 6.5000
## 46 2 2 3 5 2 2 6.5000
## 47 5 3 5 6 3 4 8.0000
## 48 5 3 5 6 3 4 8.0000
## 49 5 3 5 6 3 4 8.0000
## 50 5 3 5 6 3 4 8.0000
## 51 5 3 5 6 3 4 8.0000
## 52 5 3 5 6 3 4 8.0000
## 53 5 3 5 6 3 4 8.0000
## 54 5 3 5 6 3 4 8.0000
## 55 5 3 5 6 3 4 8.0000
## 56 5 3 5 6 3 4 8.0000
## 57 5 3 5 6 3 4 8.0000
## 58 5 3 5 6 3 4 8.0000
## 59 5 6 2 5 6 3 8.0000
## 60 5 6 2 5 6 3 8.0000
## 61 5 6 2 5 6 3 8.0000
## 62 5 6 2 5 6 3 8.0000
## 63 5 6 2 5 6 3 8.0000
## 64 5 6 2 5 6 3 8.0000
## 65 5 6 2 5 6 3 8.0000
## 66 5 6 2 5 6 3 8.0000
## 67 5 6 2 5 6 3 8.0000
## 68 5 6 2 5 6 3 8.0000
## 69 5 6 2 5 6 3 8.0000
## 70 5 6 2 5 6 3 8.0000
## 71 6 3 2 3 3 2 8.0000
## 72 6 3 2 3 3 2 8.0000
## 73 6 3 2 3 3 2 8.0000
## 74 6 3 2 3 3 2 8.0000
## 75 6 3 2 3 3 2 8.0000
## 76 6 3 2 3 3 2 8.0000
## 77 6 3 2 3 3 2 8.0000
## 78 6 3 2 3 3 2 8.0000
## 79 6 3 2 3 3 2 8.0000
## 80 6 3 2 3 3 2 8.0000
## 81 5 5 3 3 5 4 6.0000
## 82 5 5 3 3 5 4 6.0000
## 83 5 5 3 3 5 4 6.0000
## 84 5 5 3 3 5 4 6.0000
## 85 5 5 3 3 5 4 6.0000
## 86 5 5 3 3 5 4 6.0000
## 87 5 5 3 3 5 4 6.0000
## 88 5 5 3 3 5 4 6.0000
## 89 5 5 3 3 5 4 6.0000
## 90 5 5 3 3 5 4 6.0000
## 91 5 5 3 3 5 4 6.0000
## 92 5 5 3 3 5 4 6.0000
## 93 7 3 2 3 3 3 7.0000
## 94 7 3 2 3 3 3 7.0000
## 95 7 3 2 3 3 3 7.0000
## 96 7 3 2 3 3 3 7.0000
## 97 7 3 2 3 3 3 7.0000
## 98 7 3 2 3 3 3 7.0000
## 99 7 3 2 3 3 3 7.0000
## 100 7 3 2 3 3 3 7.0000
## 101 7 3 2 3 3 3 7.0000
## 102 7 3 2 3 3 3 7.0000
## 103 7 3 2 3 3 3 7.0000
## 104 7 3 2 3 3 3 7.0000
## 105 4 5 2 7 5 4 10.0000
## 106 4 5 2 7 5 4 10.0000
## 107 4 5 2 7 5 4 10.0000
## 108 4 5 2 7 5 4 10.0000
## 109 4 5 2 7 5 4 10.0000
## 110 4 5 2 7 5 4 10.0000
## 111 4 5 2 7 5 4 10.0000
## 112 4 5 2 7 5 4 10.0000
## 113 4 5 2 7 5 4 10.0000
## 114 4 5 2 7 5 4 10.0000
## 115 4 5 2 7 5 4 10.0000
## 116 4 5 2 7 5 4 10.0000
## 117 5 1 1 7 2 1 8.5000
## 118 5 1 1 7 2 1 8.5000
## 119 5 1 1 7 2 1 8.5000
## 120 5 1 1 7 2 1 8.5000
## 121 5 1 1 7 2 1 8.5000
## 122 5 1 1 7 2 1 8.5000
## 123 5 1 1 7 2 1 8.5000
## 124 5 1 1 7 2 1 8.5000
## 125 5 1 1 7 2 1 8.5000
## 126 5 1 1 7 2 1 8.5000
## 127 5 1 1 7 2 1 8.5000
## 128 5 1 1 7 2 1 8.5000
## 129 7 3 3 6 4 3 8.5000
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## 131 7 3 3 6 4 3 8.5000
## 132 7 3 3 6 4 3 8.5000
## 133 7 3 3 6 4 3 8.5000
## 134 7 3 3 6 4 3 8.5000
## 135 7 3 3 6 4 3 8.5000
## 136 7 3 3 6 4 3 8.5000
## 137 7 3 3 6 4 3 8.5000
## 138 7 3 3 6 4 3 8.5000
## 139 7 3 3 6 4 3 8.5000
## 140 7 3 3 6 4 3 8.5000
## 141 6 3 1 6 6 4 8.0000
## 142 6 3 1 6 6 4 8.0000
## 143 6 3 1 6 6 4 8.0000
## 144 6 3 1 6 6 4 8.0000
## 145 6 3 1 6 6 4 8.0000
## 146 6 3 1 6 6 4 8.0000
## 147 6 3 1 6 6 4 8.0000
## 148 6 3 1 6 6 4 8.0000
## 149 6 3 1 6 6 4 8.0000
## 150 6 3 1 6 6 4 8.0000
## 151 6 3 1 6 6 4 8.0000
## 152 6 3 1 6 6 4 8.0000
## 153 4 3 3 4 3 3 8.0000
## 154 4 3 3 4 3 3 8.0000
## 155 4 3 3 4 3 3 8.0000
## 156 4 3 3 4 3 3 8.0000
## 157 4 3 3 4 3 3 8.0000
## 158 4 3 3 4 3 3 8.0000
## 159 4 3 3 4 3 3 8.0000
## 160 4 3 3 4 3 3 8.0000
## 161 4 3 3 4 3 3 8.0000
## 162 4 3 3 4 3 3 8.0000
## 163 4 3 3 4 3 3 8.0000
## 164 4 3 3 4 3 3 8.0000
## 165 7 2 5 7 2 6 7.0000
## 166 7 2 5 7 2 6 7.0000
## 167 7 2 5 7 2 6 7.0000
## 168 7 2 5 7 2 6 7.0000
## 169 7 2 5 7 2 6 7.0000
## 170 7 2 5 7 2 6 7.0000
## 171 7 2 5 7 2 6 7.0000
## 172 7 2 5 7 2 6 7.0000
## 173 7 2 5 7 2 6 7.0000
## 174 7 2 5 7 2 6 7.0000
## 175 7 2 5 7 2 6 7.0000
## 176 7 2 5 7 2 6 7.0000
## 177 5 2 5 7 3 5 8.5000
## 178 5 2 5 7 3 5 8.5000
## 179 5 2 5 7 3 5 8.5000
## 180 5 2 5 7 3 5 8.5000
## 181 5 2 5 7 3 5 8.5000
## 182 5 2 5 7 3 5 8.5000
## 183 5 2 5 7 3 5 8.5000
## 184 5 2 5 7 3 5 8.5000
## 185 5 2 5 7 3 5 8.5000
## 186 5 2 5 7 3 5 8.5000
## 187 5 2 5 7 3 5 8.5000
## 188 5 2 5 7 3 5 8.5000
## 189 5 1 5 7 1 4 6.5000
## 190 5 1 5 7 1 4 6.5000
## 191 5 1 5 7 1 4 6.5000
## 192 5 1 5 7 1 4 6.5000
## 193 5 1 5 7 1 4 6.5000
## 194 5 1 5 7 1 4 6.5000
## 195 5 1 5 7 1 4 6.5000
## 196 5 1 5 7 1 4 6.5000
## 197 5 1 5 7 1 4 6.5000
## 198 5 1 5 7 1 4 6.5000
## 199 5 1 5 7 1 4 6.5000
## 200 5 1 5 7 1 4 6.5000
## 201 7 2 3 5 2 2 5.0000
## 202 7 2 3 5 2 2 5.0000
## 203 7 2 3 5 2 2 5.0000
## 204 7 2 3 5 2 2 5.0000
## 205 7 2 3 5 2 2 5.0000
## 206 7 2 3 5 2 2 5.0000
## 207 7 2 3 5 2 2 5.0000
## 208 7 2 3 5 2 2 5.0000
## 209 7 2 3 5 2 2 5.0000
## 210 7 2 3 5 2 2 5.0000
## 211 7 2 3 5 2 2 5.0000
## 212 7 2 3 5 2 2 5.0000
## 213 0 3 3 7 4 2 8.5000
## 214 0 3 3 7 4 2 8.5000
## 215 0 3 3 7 4 2 8.5000
## 216 0 3 3 7 4 2 8.5000
## 217 0 3 3 7 4 2 8.5000
## 218 0 3 3 7 4 2 8.5000
## 219 0 3 3 7 4 2 8.5000
## 220 0 3 3 7 4 2 8.5000
## 221 0 3 3 7 4 2 8.5000
## 222 0 3 3 7 4 2 8.5000
## 223 0 3 3 7 4 2 8.5000
## 224 0 3 3 7 4 2 8.5000
## 225 4 3 5 4 2 3 7.0000
## 226 4 3 5 4 2 3 7.0000
## 227 4 3 5 4 2 3 7.0000
## 228 4 3 5 4 2 3 7.0000
## 229 4 3 5 4 2 3 7.0000
## 230 4 3 5 4 2 3 7.0000
## 231 4 3 5 4 2 3 7.0000
## 232 4 3 5 4 2 3 7.0000
## 233 4 3 5 4 2 3 7.0000
## 234 4 3 5 4 2 3 7.0000
## 235 4 3 5 4 2 3 7.0000
## 236 4 3 5 4 2 3 7.0000
## 237 7 3 4 4 3 3 9.0000
## 238 7 3 4 4 3 3 9.0000
## 239 7 3 4 4 3 3 9.0000
## 240 7 3 4 4 3 3 9.0000
## 241 7 3 4 4 3 3 9.0000
## 242 7 3 4 4 3 3 9.0000
## 243 7 3 4 4 3 3 9.0000
## 244 7 3 4 4 3 3 9.0000
## 245 7 3 4 4 3 3 9.0000
## 246 7 3 4 4 3 3 9.0000
## 247 7 3 4 4 3 3 9.0000
## 248 7 3 4 4 3 3 9.0000
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## 250 7 2 3 6 2 3 6.0000
## 251 7 2 3 6 2 3 6.0000
## 252 7 2 3 6 2 3 6.0000
## 253 7 2 3 6 2 3 6.0000
## 254 7 2 3 6 2 3 6.0000
## 255 7 2 3 6 2 3 6.0000
## 256 7 2 3 6 2 3 6.0000
## 257 7 2 3 6 2 3 6.0000
## 258 7 2 3 6 2 3 6.0000
## 259 7 2 3 6 2 3 6.0000
## 260 7 2 3 6 2 3 6.0000
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## 264 6 4 2 4 5 3 7.5000
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## 266 6 4 2 4 5 3 7.5000
## 267 6 4 2 4 5 3 7.5000
## 268 6 4 2 4 5 3 7.5000
## 269 6 4 2 4 5 3 7.5000
## 270 6 4 2 4 5 3 7.5000
## 271 6 4 2 4 5 3 7.5000
## 272 6 4 2 4 5 3 7.5000
## 273 4 6 1 3 5 4 9.0000
## 274 4 6 1 3 5 4 9.0000
## 275 4 6 1 3 5 4 9.0000
## 276 4 6 1 3 5 4 9.0000
## 277 4 6 1 3 5 4 9.0000
## 278 4 6 1 3 5 4 9.0000
## 279 4 6 1 3 5 4 9.0000
## 280 4 6 1 3 5 4 9.0000
## 281 4 6 1 3 5 4 9.0000
## 282 4 6 1 3 5 4 9.0000
## 283 4 6 1 3 5 4 9.0000
## 284 4 6 1 3 5 4 9.0000
## 285 4 3 4 5 6 3 9.0000
## 286 4 3 4 5 6 3 9.0000
## 287 4 3 4 5 6 3 9.0000
## 288 4 3 4 5 6 3 9.0000
## 289 4 3 4 5 6 3 9.0000
## 290 4 3 4 5 6 3 9.0000
## 291 4 3 4 5 6 3 9.0000
## 292 4 3 4 5 6 3 9.0000
## 293 4 3 4 5 6 3 9.0000
## 294 4 3 4 5 6 3 9.0000
## 295 4 3 4 5 6 3 9.0000
## 296 4 3 4 5 6 3 9.0000
## 297 7 2 5 5 2 4 7.0000
## 298 7 2 5 5 2 4 7.0000
## 299 7 2 5 5 2 4 7.0000
## 300 7 2 5 5 2 4 7.0000
## 301 7 2 5 5 2 4 7.0000
## 302 7 2 5 5 2 4 7.0000
## 303 7 2 5 5 2 4 7.0000
## 304 7 2 5 5 2 4 7.0000
## 305 7 2 5 5 2 4 7.0000
## 306 7 2 5 5 2 4 7.0000
## 307 7 2 5 5 2 4 7.0000
## 308 7 2 5 5 2 4 7.0000
## 309 5 5 4 4 5 4 8.0000
## 310 5 5 4 4 5 4 8.0000
## 311 5 5 4 4 5 4 8.0000
## 312 5 5 4 4 5 4 8.0000
## 313 5 5 4 4 5 4 8.0000
## 314 5 5 4 4 5 4 8.0000
## 315 5 5 4 4 5 4 8.0000
## 316 5 5 4 4 5 4 8.0000
## 317 5 5 4 4 5 4 8.0000
## 318 5 5 4 4 5 4 8.0000
## 319 5 5 4 4 5 4 8.0000
## 320 5 5 4 4 5 4 8.0000
## 321 6 3 5 6 2 2 8.0000
## 322 6 3 5 6 2 2 8.0000
## 323 6 3 5 6 2 2 8.0000
## 324 6 3 5 6 2 2 8.0000
## 325 6 3 5 6 2 2 8.0000
## 326 6 3 5 6 2 2 8.0000
## 327 6 3 5 6 2 2 8.0000
## 328 6 3 5 6 2 2 8.0000
## 329 6 3 5 6 2 2 8.0000
## 330 6 3 5 6 2 2 8.0000
## 331 2 3 2 7 3 2 9.0000
## 332 2 3 2 7 3 2 9.0000
## 333 2 3 2 7 3 2 9.0000
## 334 2 3 2 7 3 2 9.0000
## 335 2 3 2 7 3 2 9.0000
## 336 2 3 2 7 3 2 9.0000
## 337 2 3 2 7 3 2 9.0000
## 338 2 3 2 7 3 2 9.0000
## 339 2 3 2 7 3 2 9.0000
## 340 2 3 2 7 3 2 9.0000
## 341 2 3 2 7 3 2 9.0000
## 342 2 3 2 7 3 2 9.0000
## 343 5 3 2 5 3 3 8.0000
## 344 5 3 2 5 3 3 8.0000
## 345 5 3 2 5 3 3 8.0000
## 346 5 3 2 5 3 3 8.0000
## 347 5 3 2 5 3 3 8.0000
## 348 5 3 2 5 3 3 8.0000
## 349 5 3 2 5 3 3 8.0000
## 350 5 3 2 5 3 3 8.0000
## 351 5 3 2 5 3 3 8.0000
## 352 5 3 2 5 3 3 8.0000
## 353 5 3 2 5 3 3 8.0000
## 354 5 3 2 5 3 3 8.0000
## 355 7 1 3 7 1 3 8.8333
## 356 7 1 3 7 1 3 8.8333
## 357 7 1 3 7 1 3 8.8333
## 358 7 1 3 7 1 3 8.8333
## 359 7 1 3 7 1 3 8.8333
## 360 7 1 3 7 1 3 8.8333
## 361 7 1 3 7 1 3 8.8333
## 362 7 1 3 7 1 3 8.8333
## 363 7 1 3 7 1 3 8.8333
## 364 7 1 3 7 1 3 8.8333
## 365 7 1 3 7 1 3 8.8333
## 366 7 1 3 7 1 3 8.8333
## 367 5 5 3 5 4 6 12.0000
## 368 5 5 3 5 4 6 12.0000
## 369 5 5 3 5 4 6 12.0000
## 370 5 5 3 5 4 6 12.0000
## 371 5 5 3 5 4 6 12.0000
## 372 5 5 3 5 4 6 12.0000
## 373 5 5 3 5 4 6 12.0000
## 374 5 5 3 5 4 6 12.0000
## 375 5 5 3 5 4 6 12.0000
## 376 5 5 3 5 4 6 12.0000
## 377 5 5 3 5 4 6 12.0000
## 378 5 5 3 5 4 6 12.0000
## 379 6 4 3 6 5 3 4.5000
## 380 6 4 3 6 5 3 4.5000
## 381 6 4 3 6 5 3 4.5000
## 382 6 4 3 6 5 3 4.5000
## 383 6 4 3 6 5 3 4.5000
## 384 6 4 3 6 5 3 4.5000
## 385 6 4 3 6 5 3 4.5000
## 386 6 4 3 6 5 3 4.5000
## 387 6 4 3 6 5 3 4.5000
## 388 6 4 3 6 5 3 4.5000
## 389 6 4 3 6 5 3 4.5000
## 390 6 4 3 6 5 3 4.5000
## 391 4 3 3 7 6 5 6.0000
## 392 4 3 3 7 6 5 6.0000
## 393 4 3 3 7 6 5 6.0000
## 394 4 3 3 7 6 5 6.0000
## 395 4 3 3 7 6 5 6.0000
## 396 4 3 3 7 6 5 6.0000
## 397 4 3 3 7 6 5 6.0000
## 398 4 3 3 7 6 5 6.0000
## 399 4 3 3 7 6 5 6.0000
## 400 4 3 3 7 6 5 6.0000
## 401 4 3 3 7 6 5 6.0000
## 402 4 3 3 7 6 5 6.0000
## 403 5 3 2 4 5 3 7.0000
## 404 5 3 2 4 5 3 7.0000
## 405 5 3 2 4 5 3 7.0000
## 406 5 3 2 4 5 3 7.0000
## 407 5 3 2 4 5 3 7.0000
## 408 5 3 2 4 5 3 7.0000
## 409 5 3 2 4 5 3 7.0000
## 410 5 3 2 4 5 3 7.0000
## 411 5 3 2 4 5 3 7.0000
## 412 5 3 2 4 5 3 7.0000
## 413 5 3 2 4 5 3 7.0000
## 414 5 3 2 4 5 3 7.0000
## 415 6 3 2 4 3 4 8.0000
## 416 6 3 2 4 3 4 8.0000
## 417 6 3 2 4 3 4 8.0000
## 418 6 3 2 4 3 4 8.0000
## 419 6 3 2 4 3 4 8.0000
## 420 6 3 2 4 3 4 8.0000
## 421 6 3 2 4 3 4 8.0000
## 422 6 3 2 4 3 4 8.0000
## 423 6 3 2 4 3 4 8.0000
## 424 6 3 2 4 3 4 8.0000
## 425 6 3 2 4 3 4 8.0000
## 426 6 3 2 4 3 4 8.0000
## 427 7 6 2 3 6 5 9.0000
## 428 7 6 2 3 6 5 9.0000
## 429 7 6 2 3 6 5 9.0000
## 430 7 6 2 3 6 5 9.0000
## 431 7 6 2 3 6 5 9.0000
## 432 7 6 2 3 6 5 9.0000
## 433 7 6 2 3 6 5 9.0000
## 434 7 6 2 3 6 5 9.0000
## 435 7 6 2 3 6 5 9.0000
## 436 7 6 2 3 6 5 9.0000
## 437 7 6 2 3 6 5 9.0000
## 438 7 6 2 3 6 5 9.0000
## 439 6 5 2 5 5 2 7.5000
## 440 6 5 2 5 5 2 7.5000
## 441 6 5 2 5 5 2 7.5000
## 442 6 5 2 5 5 2 7.5000
## 443 6 5 2 5 5 2 7.5000
## 444 6 5 2 5 5 2 7.5000
## 445 6 5 2 5 5 2 7.5000
## 446 6 5 2 5 5 2 7.5000
## 447 6 5 2 5 5 2 7.5000
## 448 6 5 2 5 5 2 7.5000
## 449 6 5 2 5 5 2 7.5000
## 450 6 5 2 5 5 2 7.5000
## 451 7 5 2 5 4 4 6.0000
## 452 7 5 2 5 4 4 6.0000
## 453 7 5 2 5 4 4 6.0000
## 454 7 5 2 5 4 4 6.0000
## 455 7 5 2 5 4 4 6.0000
## 456 7 5 2 5 4 4 6.0000
## 457 7 5 2 5 4 4 6.0000
## 458 7 5 2 5 4 4 6.0000
## 459 7 5 2 5 4 4 6.0000
## 460 7 5 2 5 4 4 6.0000
## 461 7 5 2 5 4 4 6.0000
## 462 7 5 2 5 4 4 6.0000
## 463 6 6 2 3 6 3 9.0000
## 464 6 6 2 3 6 3 9.0000
## 465 6 6 2 3 6 3 9.0000
## 466 6 6 2 3 6 3 9.0000
## 467 6 6 2 3 6 3 9.0000
## 468 6 6 2 3 6 3 9.0000
## 469 6 6 2 3 6 3 9.0000
## 470 6 6 2 3 6 3 9.0000
## 471 6 6 2 3 6 3 9.0000
## 472 6 6 2 3 6 3 9.0000
## 473 6 6 2 3 6 3 9.0000
## 474 6 6 2 3 6 3 9.0000
## 475 4 7 2 5 7 6 8.0000
## 476 4 7 2 5 7 6 8.0000
## 477 4 7 2 5 7 6 8.0000
## 478 4 7 2 5 7 6 8.0000
## 479 4 7 2 5 7 6 8.0000
## 480 4 7 2 5 7 6 8.0000
## 481 4 7 2 5 7 6 8.0000
## 482 4 7 2 5 7 6 8.0000
## 483 4 7 2 5 7 6 8.0000
## 484 4 7 2 5 7 6 8.0000
## 485 4 7 2 5 7 6 8.0000
## 486 4 7 2 5 7 6 8.0000
## 487 5 5 2 5 4 4 7.5000
## 488 5 5 2 5 4 4 7.5000
## 489 5 5 2 5 4 4 7.5000
## 490 5 5 2 5 4 4 7.5000
## 491 5 5 2 5 4 4 7.5000
## 492 5 5 2 5 4 4 7.5000
## 493 5 5 2 5 4 4 7.5000
## 494 5 5 2 5 4 4 7.5000
## 495 5 5 2 5 4 4 7.5000
## 496 5 5 2 5 4 4 7.5000
## 497 5 5 2 5 4 4 7.5000
## 498 5 5 2 5 4 4 7.5000
## 499 7 2 1 7 3 5 6.0000
## 500 7 2 1 7 3 5 6.0000
## 501 7 2 1 7 3 5 6.0000
## 502 7 2 1 7 3 5 6.0000
## 503 7 2 1 7 3 5 6.0000
## 504 7 2 1 7 3 5 6.0000
## 505 7 2 1 7 3 5 6.0000
## 506 7 2 1 7 3 5 6.0000
## 507 7 2 1 7 3 5 6.0000
## 508 7 2 1 7 3 5 6.0000
## 509 7 2 1 7 3 5 6.0000
## 510 7 2 1 7 3 5 6.0000
## 511 5 3 3 3 4 3 7.5000
## 512 5 3 3 3 4 3 7.5000
## 513 5 3 3 3 4 3 7.5000
## 514 5 3 3 3 4 3 7.5000
## 515 5 3 3 3 4 3 7.5000
## 516 5 3 3 3 4 3 7.5000
## 517 5 3 3 3 4 3 7.5000
## 518 5 3 3 3 4 3 7.5000
## 519 5 3 3 3 4 3 7.5000
## 520 5 3 3 3 4 3 7.5000
## 521 5 3 3 3 4 3 7.5000
## 522 5 3 3 3 4 3 7.5000
## 523 4 4 2 4 5 6 6.0000
## 524 4 4 2 4 5 6 6.0000
## 525 4 4 2 4 5 6 6.0000
## 526 4 4 2 4 5 6 6.0000
## 527 4 4 2 4 5 6 6.0000
## 528 4 4 2 4 5 6 6.0000
## 529 4 4 2 4 5 6 6.0000
## 530 4 4 2 4 5 6 6.0000
## 531 4 4 2 4 5 6 6.0000
## 532 4 4 2 4 5 6 6.0000
## 533 4 4 2 4 5 6 6.0000
## 534 4 4 2 4 5 6 6.0000
## 535 5 6 2 3 5 4 9.5000
## 536 5 6 2 3 5 4 9.5000
## 537 5 6 2 3 5 4 9.5000
## 538 5 6 2 3 5 4 9.5000
## 539 5 6 2 3 5 4 9.5000
## 540 5 6 2 3 5 4 9.5000
## 541 5 6 2 3 5 4 9.5000
## 542 5 6 2 3 5 4 9.5000
## 543 5 6 2 3 5 4 9.5000
## 544 5 6 2 3 5 4 9.5000
## 545 5 6 2 3 5 4 9.5000
## 546 5 6 2 3 5 4 9.5000
## 547 6 2 6 7 2 3 9.0000
## 548 6 2 6 7 2 3 9.0000
## 549 6 2 6 7 2 3 9.0000
## 550 6 2 6 7 2 3 9.0000
## 551 6 2 6 7 2 3 9.0000
## 552 6 2 6 7 2 3 9.0000
## 553 6 2 6 7 2 3 9.0000
## 554 6 2 6 7 2 3 9.0000
## 555 6 2 6 7 2 3 9.0000
## 556 6 2 6 7 2 3 9.0000
## 557 6 2 6 7 2 3 9.0000
## 558 6 2 6 7 2 3 9.0000
## modality stress_group
## 1 fmri control
## 2 fmri control
## 3 fmri control
## 4 fmri control
## 5 fmri control
## 6 fmri control
## 7 fmri control
## 8 fmri control
## 9 fmri control
## 10 fmri control
## 11 fmri control
## 12 fmri control
## 13 fmri control
## 14 fmri control
## 15 fmri control
## 16 fmri control
## 17 fmri control
## 18 fmri control
## 19 fmri control
## 20 fmri control
## 21 fmri control
## 22 fmri control
## 23 fmri control
## 24 fmri control
## 25 fmri control
## 26 fmri control
## 27 fmri control
## 28 fmri control
## 29 fmri control
## 30 fmri control
## 31 fmri control
## 32 fmri control
## 33 fmri control
## 34 fmri control
## 35 fmri control
## 36 fmri control
## 37 fmri control
## 38 fmri control
## 39 fmri control
## 40 fmri control
## 41 fmri control
## 42 fmri control
## 43 fmri control
## 44 fmri control
## 45 fmri control
## 46 fmri control
## 47 fmri control
## 48 fmri control
## 49 fmri control
## 50 fmri control
## 51 fmri control
## 52 fmri control
## 53 fmri control
## 54 fmri control
## 55 fmri control
## 56 fmri control
## 57 fmri control
## 58 fmri control
## 59 fmri control
## 60 fmri control
## 61 fmri control
## 62 fmri control
## 63 fmri control
## 64 fmri control
## 65 fmri control
## 66 fmri control
## 67 fmri control
## 68 fmri control
## 69 fmri control
## 70 fmri control
## 71 fmri control
## 72 fmri control
## 73 fmri control
## 74 fmri control
## 75 fmri control
## 76 fmri control
## 77 fmri control
## 78 fmri control
## 79 fmri control
## 80 fmri control
## 81 fmri control
## 82 fmri control
## 83 fmri control
## 84 fmri control
## 85 fmri control
## 86 fmri control
## 87 fmri control
## 88 fmri control
## 89 fmri control
## 90 fmri control
## 91 fmri control
## 92 fmri control
## 93 fmri control
## 94 fmri control
## 95 fmri control
## 96 fmri control
## 97 fmri control
## 98 fmri control
## 99 fmri control
## 100 fmri control
## 101 fmri control
## 102 fmri control
## 103 fmri control
## 104 fmri control
## 105 fmri control
## 106 fmri control
## 107 fmri control
## 108 fmri control
## 109 fmri control
## 110 fmri control
## 111 fmri control
## 112 fmri control
## 113 fmri control
## 114 fmri control
## 115 fmri control
## 116 fmri control
## 117 fmri control
## 118 fmri control
## 119 fmri control
## 120 fmri control
## 121 fmri control
## 122 fmri control
## 123 fmri control
## 124 fmri control
## 125 fmri control
## 126 fmri control
## 127 fmri control
## 128 fmri control
## 129 fmri control
## 130 fmri control
## 131 fmri control
## 132 fmri control
## 133 fmri control
## 134 fmri control
## 135 fmri control
## 136 fmri control
## 137 fmri control
## 138 fmri control
## 139 fmri control
## 140 fmri control
## 141 fmri control
## 142 fmri control
## 143 fmri control
## 144 fmri control
## 145 fmri control
## 146 fmri control
## 147 fmri control
## 148 fmri control
## 149 fmri control
## 150 fmri control
## 151 fmri control
## 152 fmri control
## 153 fmri control
## 154 fmri control
## 155 fmri control
## 156 fmri control
## 157 fmri control
## 158 fmri control
## 159 fmri control
## 160 fmri control
## 161 fmri control
## 162 fmri control
## 163 fmri control
## 164 fmri control
## 165 fmri control
## 166 fmri control
## 167 fmri control
## 168 fmri control
## 169 fmri control
## 170 fmri control
## 171 fmri control
## 172 fmri control
## 173 fmri control
## 174 fmri control
## 175 fmri control
## 176 fmri control
## 177 fmri control
## 178 fmri control
## 179 fmri control
## 180 fmri control
## 181 fmri control
## 182 fmri control
## 183 fmri control
## 184 fmri control
## 185 fmri control
## 186 fmri control
## 187 fmri control
## 188 fmri control
## 189 fmri control
## 190 fmri control
## 191 fmri control
## 192 fmri control
## 193 fmri control
## 194 fmri control
## 195 fmri control
## 196 fmri control
## 197 fmri control
## 198 fmri control
## 199 fmri control
## 200 fmri control
## 201 fmri control
## 202 fmri control
## 203 fmri control
## 204 fmri control
## 205 fmri control
## 206 fmri control
## 207 fmri control
## 208 fmri control
## 209 fmri control
## 210 fmri control
## 211 fmri control
## 212 fmri control
## 213 fmri control
## 214 fmri control
## 215 fmri control
## 216 fmri control
## 217 fmri control
## 218 fmri control
## 219 fmri control
## 220 fmri control
## 221 fmri control
## 222 fmri control
## 223 fmri control
## 224 fmri control
## 225 fmri control
## 226 fmri control
## 227 fmri control
## 228 fmri control
## 229 fmri control
## 230 fmri control
## 231 fmri control
## 232 fmri control
## 233 fmri control
## 234 fmri control
## 235 fmri control
## 236 fmri control
## 237 fmri control
## 238 fmri control
## 239 fmri control
## 240 fmri control
## 241 fmri control
## 242 fmri control
## 243 fmri control
## 244 fmri control
## 245 fmri control
## 246 fmri control
## 247 fmri control
## 248 fmri control
## 249 fmri control
## 250 fmri control
## 251 fmri control
## 252 fmri control
## 253 fmri control
## 254 fmri control
## 255 fmri control
## 256 fmri control
## 257 fmri control
## 258 fmri control
## 259 fmri control
## 260 fmri control
## 261 fmri stress
## 262 fmri stress
## 263 fmri stress
## 264 fmri stress
## 265 fmri stress
## 266 fmri stress
## 267 fmri stress
## 268 fmri stress
## 269 fmri stress
## 270 fmri stress
## 271 fmri stress
## 272 fmri stress
## 273 fmri stress
## 274 fmri stress
## 275 fmri stress
## 276 fmri stress
## 277 fmri stress
## 278 fmri stress
## 279 fmri stress
## 280 fmri stress
## 281 fmri stress
## 282 fmri stress
## 283 fmri stress
## 284 fmri stress
## 285 fmri stress
## 286 fmri stress
## 287 fmri stress
## 288 fmri stress
## 289 fmri stress
## 290 fmri stress
## 291 fmri stress
## 292 fmri stress
## 293 fmri stress
## 294 fmri stress
## 295 fmri stress
## 296 fmri stress
## 297 fmri stress
## 298 fmri stress
## 299 fmri stress
## 300 fmri stress
## 301 fmri stress
## 302 fmri stress
## 303 fmri stress
## 304 fmri stress
## 305 fmri stress
## 306 fmri stress
## 307 fmri stress
## 308 fmri stress
## 309 fmri stress
## 310 fmri stress
## 311 fmri stress
## 312 fmri stress
## 313 fmri stress
## 314 fmri stress
## 315 fmri stress
## 316 fmri stress
## 317 fmri stress
## 318 fmri stress
## 319 fmri stress
## 320 fmri stress
## 321 fmri stress
## 322 fmri stress
## 323 fmri stress
## 324 fmri stress
## 325 fmri stress
## 326 fmri stress
## 327 fmri stress
## 328 fmri stress
## 329 fmri stress
## 330 fmri stress
## 331 fmri stress
## 332 fmri stress
## 333 fmri stress
## 334 fmri stress
## 335 fmri stress
## 336 fmri stress
## 337 fmri stress
## 338 fmri stress
## 339 fmri stress
## 340 fmri stress
## 341 fmri stress
## 342 fmri stress
## 343 fmri stress
## 344 fmri stress
## 345 fmri stress
## 346 fmri stress
## 347 fmri stress
## 348 fmri stress
## 349 fmri stress
## 350 fmri stress
## 351 fmri stress
## 352 fmri stress
## 353 fmri stress
## 354 fmri stress
## 355 fmri control
## 356 fmri control
## 357 fmri control
## 358 fmri control
## 359 fmri control
## 360 fmri control
## 361 fmri control
## 362 fmri control
## 363 fmri control
## 364 fmri control
## 365 fmri control
## 366 fmri control
## 367 fmri stress
## 368 fmri stress
## 369 fmri stress
## 370 fmri stress
## 371 fmri stress
## 372 fmri stress
## 373 fmri stress
## 374 fmri stress
## 375 fmri stress
## 376 fmri stress
## 377 fmri stress
## 378 fmri stress
## 379 fmri stress
## 380 fmri stress
## 381 fmri stress
## 382 fmri stress
## 383 fmri stress
## 384 fmri stress
## 385 fmri stress
## 386 fmri stress
## 387 fmri stress
## 388 fmri stress
## 389 fmri stress
## 390 fmri stress
## 391 fmri stress
## 392 fmri stress
## 393 fmri stress
## 394 fmri stress
## 395 fmri stress
## 396 fmri stress
## 397 fmri stress
## 398 fmri stress
## 399 fmri stress
## 400 fmri stress
## 401 fmri stress
## 402 fmri stress
## 403 fmri stress
## 404 fmri stress
## 405 fmri stress
## 406 fmri stress
## 407 fmri stress
## 408 fmri stress
## 409 fmri stress
## 410 fmri stress
## 411 fmri stress
## 412 fmri stress
## 413 fmri stress
## 414 fmri stress
## 415 fmri stress
## 416 fmri stress
## 417 fmri stress
## 418 fmri stress
## 419 fmri stress
## 420 fmri stress
## 421 fmri stress
## 422 fmri stress
## 423 fmri stress
## 424 fmri stress
## 425 fmri stress
## 426 fmri stress
## 427 fmri stress
## 428 fmri stress
## 429 fmri stress
## 430 fmri stress
## 431 fmri stress
## 432 fmri stress
## 433 fmri stress
## 434 fmri stress
## 435 fmri stress
## 436 fmri stress
## 437 fmri stress
## 438 fmri stress
## 439 fmri stress
## 440 fmri stress
## 441 fmri stress
## 442 fmri stress
## 443 fmri stress
## 444 fmri stress
## 445 fmri stress
## 446 fmri stress
## 447 fmri stress
## 448 fmri stress
## 449 fmri stress
## 450 fmri stress
## 451 fmri stress
## 452 fmri stress
## 453 fmri stress
## 454 fmri stress
## 455 fmri stress
## 456 fmri stress
## 457 fmri stress
## 458 fmri stress
## 459 fmri stress
## 460 fmri stress
## 461 fmri stress
## 462 fmri stress
## 463 fmri stress
## 464 fmri stress
## 465 fmri stress
## 466 fmri stress
## 467 fmri stress
## 468 fmri stress
## 469 fmri stress
## 470 fmri stress
## 471 fmri stress
## 472 fmri stress
## 473 fmri stress
## 474 fmri stress
## 475 fmri stress
## 476 fmri stress
## 477 fmri stress
## 478 fmri stress
## 479 fmri stress
## 480 fmri stress
## 481 fmri stress
## 482 fmri stress
## 483 fmri stress
## 484 fmri stress
## 485 fmri stress
## 486 fmri stress
## 487 fmri stress
## 488 fmri stress
## 489 fmri stress
## 490 fmri stress
## 491 fmri stress
## 492 fmri stress
## 493 fmri stress
## 494 fmri stress
## 495 fmri stress
## 496 fmri stress
## 497 fmri stress
## 498 fmri stress
## 499 fmri stress
## 500 fmri stress
## 501 fmri stress
## 502 fmri stress
## 503 fmri stress
## 504 fmri stress
## 505 fmri stress
## 506 fmri stress
## 507 fmri stress
## 508 fmri stress
## 509 fmri stress
## 510 fmri stress
## 511 fmri stress
## 512 fmri stress
## 513 fmri stress
## 514 fmri stress
## 515 fmri stress
## 516 fmri stress
## 517 fmri stress
## 518 fmri stress
## 519 fmri stress
## 520 fmri stress
## 521 fmri stress
## 522 fmri stress
## 523 fmri stress
## 524 fmri stress
## 525 fmri stress
## 526 fmri stress
## 527 fmri stress
## 528 fmri stress
## 529 fmri stress
## 530 fmri stress
## 531 fmri stress
## 532 fmri stress
## 533 fmri stress
## 534 fmri stress
## 535 fmri stress
## 536 fmri stress
## 537 fmri stress
## 538 fmri stress
## 539 fmri stress
## 540 fmri stress
## 541 fmri stress
## 542 fmri stress
## 543 fmri stress
## 544 fmri stress
## 545 fmri stress
## 546 fmri stress
## 547 fmri stress
## 548 fmri stress
## 549 fmri stress
## 550 fmri stress
## 551 fmri stress
## 552 fmri stress
## 553 fmri stress
## 554 fmri stress
## 555 fmri stress
## 556 fmri stress
## 557 fmri stress
## 558 fmri stress
## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control-fmri 23
## 2 stress-fmri 24## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## adjustedRating ~ subScaleName * group * shockCond + (1 + subScaleName *
## shockCond | subid)
## Data: d_val
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 1103.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2399 -0.5895 -0.0305 0.5562 3.2341
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.156649 0.39579
## subScaleName1 0.141987 0.37681 -0.52
## shockCond1 0.003978 0.06307 0.13 -0.42
## subScaleName1:shockCond1 0.013973 0.11821 0.38 -0.11 -0.82
## Residual 0.280223 0.52936
## Number of obs: 558, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.48546 0.06195 44.98000 40.120
## subScaleName1 0.25591 0.05938 44.85000 4.310
## group1 -0.04575 0.06195 44.98000 -0.738
## shockCond1 0.06393 0.02425 84.91000 2.636
## subScaleName1:group1 -0.15614 0.05938 44.85000 -2.629
## subScaleName1:shockCond1 -0.12646 0.02831 47.72000 -4.468
## group1:shockCond1 0.02509 0.02425 84.91000 1.035
## subScaleName1:group1:shockCond1 -0.10363 0.02831 47.72000 -3.661
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## subScaleName1 8.82e-05 ***
## group1 0.464083
## shockCond1 0.009973 **
## subScaleName1:group1 0.011674 *
## subScaleName1:shockCond1 4.85e-05 ***
## group1:shockCond1 0.303805
## subScaleName1:group1:shockCond1 0.000627 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sbScN1 group1 shckC1 sbSN1:1 sSN1:C gr1:C1
## subScaleNm1 -0.446
## group1 -0.022 0.010
## shockCond1 0.046 -0.148 0.000
## sbSclNm1:g1 0.010 -0.022 -0.446 0.003
## sbSclNm1:C1 0.213 -0.063 -0.005 -0.190 0.003
## grp1:shckC1 0.000 0.003 0.046 -0.025 -0.148 0.004
## sbScN1:1:C1 -0.005 0.003 0.213 0.004 -0.063 -0.024 -0.190## Source: local data frame [282 x 6]
## Groups: subScaleName, shockCond [6]
##
## # A tibble: 282 x 6
## group study subScaleName shockCond subid adjustedRating
## <chr> <chr> <fctr> <fctr> <fctr> <dbl>
## 1 Control fmri Arousal safe ap100 1.500000
## 2 Control fmri Arousal safe ap101 3.333333
## 3 Control fmri Arousal safe ap102 3.000000
## 4 Control fmri Arousal safe ap103 1.666667
## 5 Control fmri Arousal safe ap104 2.000000
## 6 Control fmri Arousal safe ap105 2.666667
## 7 Control fmri Arousal safe ap106 3.333333
## 8 Control fmri Arousal safe ap107 2.000000
## 9 Control fmri Arousal safe ap108 1.666667
## 10 Control fmri Arousal safe ap109 2.333333
## # ... with 272 more rows## Source: local data frame [12 x 7]
## Groups: group, subScaleName [?]
##
## # A tibble: 12 x 7
## group subScaleName shockCond n mean sd se
## <chr> <fctr> <fctr> <int> <dbl> <dbl> <dbl>
## 1 Control Arousal safe 23 2.268116 0.6566273 0.13691626
## 2 Control Arousal threat 23 2.405797 0.7518094 0.15676310
## 3 Control Negative safe 23 2.057971 0.7547973 0.15738611
## 4 Control Negative threat 23 2.181159 0.6925160 0.14439957
## 5 Control Positive safe 23 2.923913 0.3980009 0.08298892
## 6 Control Positive threat 23 2.960145 0.4050421 0.08445711
## 7 Stress Arousal safe 24 1.986111 0.6701299 0.13678969
## 8 Stress Arousal threat 24 2.791667 0.7601964 0.15517444
## 9 Stress Negative safe 24 2.020833 0.7853005 0.16029879
## 10 Stress Negative threat 24 2.652778 0.8059511 0.16451408
## 11 Stress Positive safe 24 2.680556 0.5426644 0.11077092
## 12 Stress Positive threat 24 2.402778 0.5771759 0.11781554
## Warning: Ignoring unknown aesthetics: y
head(d_val)## subid BAS.D BAS.F BAS.W BIS SMM TraitAnx group run
## 1 ap100 3.5 3.75 3.2 3.714286 2.75 2.8 control-fmri 1
## 2 ap100 3.5 3.75 3.2 3.714286 2.75 2.8 control-fmri 1
## 3 ap100 3.5 3.75 3.2 3.714286 2.75 2.8 control-fmri 2
## 4 ap100 3.5 3.75 3.2 3.714286 2.75 2.8 control-fmri 2
## 5 ap100 3.5 3.75 3.2 3.714286 2.75 2.8 control-fmri 4
## 6 ap100 3.5 3.75 3.2 3.714286 2.75 2.8 control-fmri 4
## shockCond subScaleName adjustedRating respRT remove good_math
## 1 safe Negative 1.5 3.91895 NA 5
## 2 safe Positive 2.5 3.09475 NA 5
## 3 threat Negative 2.5 1.32142 NA 5
## 4 threat Positive 2.5 3.32340 NA 5
## 5 threat Negative 2.0 3.21695 NA 5
## 6 threat Positive 2.5 1.80115 NA 5
## important_math anxious happy safe stressed life_stress sleep modality
## 1 6 3 2 3 3 4 4.5 fmri
## 2 6 3 2 3 3 4 4.5 fmri
## 3 6 3 2 3 3 4 4.5 fmri
## 4 6 3 2 3 3 4 4.5 fmri
## 5 6 3 2 3 3 4 4.5 fmri
## 6 6 3 2 3 3 4 4.5 fmri
## stress_group
## 1 control
## 2 control
## 3 control
## 4 control
## 5 control
## 6 controldata = d_val %>% group_by(subid, group, subScaleName, shockCond) %>%
summarise(adjustedRating = mean(adjustedRating))
# control diff?
bartlett.test(adjustedRating ~ shockCond, data=data[(data$subScaleName == 'Negative') &
(data$group == 'control-fmri'),])##
## Bartlett test of homogeneity of variances
##
## data: adjustedRating by shockCond
## Bartlett's K-squared = 0.15934, df = 1, p-value = 0.6898t.test(adjustedRating ~ shockCond, data=data[(data$subScaleName == 'Negative') &
(data$group == 'control-fmri'),],
paired=T, var.equal=TRUE)##
## Paired t-test
##
## data: adjustedRating by shockCond
## t = -1.6931, df = 22, p-value = 0.1046
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.27408260 0.02770578
## sample estimates:
## mean of the differences
## -0.1231884bartlett.test(adjustedRating ~ shockCond, data=data[(data$subScaleName == 'Positive') &
(data$group == 'control-fmri'),])##
## Bartlett test of homogeneity of variances
##
## data: adjustedRating by shockCond
## Bartlett's K-squared = 0.0066151, df = 1, p-value = 0.9352t.test(adjustedRating ~ shockCond, data=data[(data$subScaleName == 'Positive') &
(data$group == 'control-fmri'),],
paired=T, var.equal=TRUE)##
## Paired t-test
##
## data: adjustedRating by shockCond
## t = -0.63983, df = 22, p-value = 0.5289
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.15367041 0.08120664
## sample estimates:
## mean of the differences
## -0.03623188# stress diff?
bartlett.test(adjustedRating ~ shockCond, data=data[(data$subScaleName == 'Negative') &
(data$group == 'stress-fmri'),])##
## Bartlett test of homogeneity of variances
##
## data: adjustedRating by shockCond
## Bartlett's K-squared = 0.015165, df = 1, p-value = 0.902t.test(adjustedRating ~ shockCond, data=data[(data$subScaleName == 'Negative') &
(data$group == 'stress-fmri'),],
paired=T, var.equal=TRUE)##
## Paired t-test
##
## data: adjustedRating by shockCond
## t = -4.4433, df = 23, p-value = 0.0001864
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.9261551 -0.3377338
## sample estimates:
## mean of the differences
## -0.6319444bartlett.test(adjustedRating ~ shockCond, data=data[(data$subScaleName == 'Positive') &
(data$group == 'stress-fmri'),])##
## Bartlett test of homogeneity of variances
##
## data: adjustedRating by shockCond
## Bartlett's K-squared = 0.085519, df = 1, p-value = 0.77t.test(adjustedRating ~ shockCond, data=data[(data$subScaleName == 'Positive') &
(data$group == 'stress-fmri'),],
paired=T, var.equal=TRUE)##
## Paired t-test
##
## data: adjustedRating by shockCond
## t = 2.5103, df = 23, p-value = 0.01955
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.04886574 0.50668981
## sample estimates:
## mean of the differences
## 0.2777778# other
bartlett.test(adjustedRating ~ group, data=data[(data$subScaleName == 'Negative') &
(data$shockCond == 'threat'),])##
## Bartlett test of homogeneity of variances
##
## data: adjustedRating by group
## Bartlett's K-squared = 0.50317, df = 1, p-value = 0.4781t.test(adjustedRating ~ group, data=data[(data$subScaleName == 'Negative') &
(data$shockCond == 'threat'),], var.equal=TRUE)##
## Two Sample t-test
##
## data: adjustedRating by group
## t = -2.1475, df = 45, p-value = 0.03718
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.91394633 -0.02929038
## sample estimates:
## mean in group control-fmri mean in group stress-fmri
## 2.181159 2.652778t.test(adjustedRating ~ group, data=data[(data$subScaleName == 'Negative') &
(data$shockCond == 'safe'),], var.equal=TRUE)##
## Two Sample t-test
##
## data: adjustedRating by group
## t = 0.16517, df = 45, p-value = 0.8695
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4157136 0.4899890
## sample estimates:
## mean in group control-fmri mean in group stress-fmri
## 2.057971 2.020833t.test(adjustedRating ~ group, data=data[(data$subScaleName == 'Positive') &
(data$shockCond == 'threat'),], var.equal=TRUE)##
## Two Sample t-test
##
## data: adjustedRating by group
## t = 3.8166, df = 45, p-value = 0.0004106
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.2632349 0.8514994
## sample estimates:
## mean in group control-fmri mean in group stress-fmri
## 2.960145 2.402778t.test(adjustedRating ~ group, data=data[(data$subScaleName == 'Positive') &
(data$shockCond == 'safe'),], var.equal=TRUE)##
## Two Sample t-test
##
## data: adjustedRating by group
## t = 1.7468, df = 45, p-value = 0.0875
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.0372423 0.5239573
## sample estimates:
## mean in group control-fmri mean in group stress-fmri
## 2.923913 2.680556Do ratings on in-scanner arousal questionnaires differ between groups?
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: adjustedRating ~ group * shockCond + (1 + shockCond | subid)
## Data: d_aro
##
## REML criterion at convergence: 666.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.47918 -0.49308 -0.06321 0.59289 2.64093
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.33299 0.5771
## shockCond1 0.01487 0.1219 0.41
## Residual 0.45112 0.6717
## Number of obs: 279, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.36782 0.09332 44.71000 25.372 < 2e-16 ***
## group1 0.02531 0.09332 44.71000 0.271 0.787481
## shockCond1 0.23997 0.04402 43.74000 5.451 2.18e-06 ***
## group1:shockCond1 0.16705 0.04402 43.74000 3.795 0.000451 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 shckC1
## group1 -0.022
## shockCond1 0.151 -0.002
## grp1:shckC1 -0.002 0.151 -0.025##
## Bartlett test of homogeneity of variances
##
## data: adjustedRating by group
## Bartlett's K-squared = 0.0027071, df = 1, p-value = 0.9585##
## Two Sample t-test
##
## data: adjustedRating by group
## t = -1.749, df = 45, p-value = 0.08712
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.83023965 0.05850052
## sample estimates:
## mean in group control-fmri mean in group stress-fmri
## 2.405797 2.791667##
## Bartlett test of homogeneity of variances
##
## data: adjustedRating by group
## Bartlett's K-squared = 0.0091116, df = 1, p-value = 0.924##
## Two Sample t-test
##
## data: adjustedRating by group
## t = 1.4564, df = 45, p-value = 0.1522
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1079760 0.6719857
## sample estimates:
## mean in group control-fmri mean in group stress-fmri
## 2.268116 1.986111##
## Bartlett test of homogeneity of variances
##
## data: adjustedRating by shockCond
## Bartlett's K-squared = 0.39297, df = 1, p-value = 0.5307##
## Paired t-test
##
## data: adjustedRating by shockCond
## t = -1.3097, df = 22, p-value = 0.2038
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.35569408 0.08033176
## sample estimates:
## mean of the differences
## -0.1376812##
## Bartlett test of homogeneity of variances
##
## data: adjustedRating by shockCond
## Bartlett's K-squared = 0.35703, df = 1, p-value = 0.5502##
## Paired t-test
##
## data: adjustedRating by shockCond
## t = -5.6265, df = 23, p-value = 9.998e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.1017267 -0.5093844
## sample estimates:
## mean of the differences
## -0.8055556Do ratings on post-test valence questionnaires differ between groups?
## X subid group gender remove anxious happy safe stressed
## 1 0 ap100 control-fmri male NA 3 2 3 3
## 2 1 ap101 control-fmri male NA 2 5 6 2
## 3 2 ap102 control-fmri male NA 4 2 5 5
## 4 3 ap103 control-fmri male NA 2 3 5 2
## 5 4 ap104 control-fmri male NA 3 5 6 3
## 6 5 ap105 control-fmri male NA 6 2 5 6
## life_stress sleep net_neg negative positive
## 1 4 4.5 1 3.0 2.5
## 2 2 8.5 -7 2.0 5.5
## 3 4 9.0 2 4.5 3.5
## 4 2 6.5 -4 2.0 4.0
## 5 4 8.0 -5 3.0 5.5
## 6 3 8.0 5 6.0 3.5## [1] 94 14## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: emo_rating ~ scale * group + (1 | subid)
## Data: d_ratings_long
##
## REML criterion at convergence: 307.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.84661 -0.61228 0.01801 0.63648 2.50316
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.000 0.000
## Residual 1.457 1.207
## Number of obs: 94, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.7939 0.1245 90.0000 30.464 < 2e-16 ***
## scale1 0.1902 0.1245 90.0000 1.527 0.130173
## group1 0.1852 0.1245 90.0000 1.487 0.140410
## scale1:group1 -0.4402 0.1245 90.0000 -3.535 0.000647 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scale1 group1
## scale1 0.000
## group1 -0.021 0.000
## scale1:grp1 0.000 -0.021 0.000##
## Bartlett test of homogeneity of variances
##
## data: negative by group
## Bartlett's K-squared = 0.00099777, df = 1, p-value = 0.9748##
## Two Sample t-test
##
## data: negative by group
## t = -3.2618, df = 45, p-value = 0.002115
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.0233118 -0.4784998
## sample estimates:
## mean in group control-fmri mean in group stress-fmri
## 2.978261 4.229167##
## Bartlett test of homogeneity of variances
##
## data: positive by group
## Bartlett's K-squared = 0.020551, df = 1, p-value = 0.886##
## Two Sample t-test
##
## data: positive by group
## t = 1.604, df = 45, p-value = 0.1157
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1303968 1.1503243
## sample estimates:
## mean in group control-fmri mean in group stress-fmri
## 4.239130 3.729167## mean(safe)
## 1 5.06383## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Post-test affective ratings, we observed an interaction between group and valence, F(1,90)=12.4948, p < 0.001, such that participants rated feeling significantly more negative (stressed, anxious), t(45) = 3.2618, p < 0.01. Positive ratings (safe, happy) did not significantly differ between groups (p > 0.1).
1) Stress effects on memory
Load in data
# note that shock and post trials are filtered out
dtest = read.csv('/Volumes/group/awagner/sgagnon/AP/data/behav/df_test.csv')
with(dtest %>% filter(acc =='H', conf == 'Hi'), table(subid))## subid
## ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ap109 ap110 ap111
## 89 105 68 113 147 119 29 125 29 61 20 73
## ap113 ap114 ap115 ap116 ap117 ap118 ap119 ap120 ap121 ap122 ap150 ap151
## 75 102 78 148 85 63 115 75 34 97 66 3
## ap152 ap153 ap154 ap155 ap156 ap157 ap158 ap159 ap160 ap161 ap162 ap163
## 44 33 46 38 5 53 11 51 51 27 44 107
## ap164 ap165 ap166 ap167 ap168 ap169 ap170 ap171 ap172 ap173 ap174
## 82 65 72 39 46 71 74 33 21 8 85with(dtest, table(acc, conf))## conf
## acc Hi Lo N
## CR 0 0 2475
## FA 216 1110 0
## H 3025 1448 0
## M 0 0 1109
## no response 0 0 140
## SM 706 1279 0head(dtest)## X run trial onset duration shockCond target associate resp
## 1 0 1 1 12.0191 10.6661 safe BANDIT cemetery indoor
## 2 1 1 2 22.7129 9.1242 safe WALRUS foil foil
## 3 2 1 3 31.8642 9.4224 safe VIOLIN throne_room indoor
## 4 3 1 4 41.3132 11.3389 safe MEDAL hayfield outdoor
## 5 4 1 5 52.6786 9.7644 safe MANURE canyon outdoor
## 6 5 1 6 62.4701 9.1180 safe SARDINE mountain_snowy outdoor
## acc accSpec respRT subid group gender remove anxious happy safe
## 1 SM SMO_Hi 2.1094 ap100 control-fmri male NA 3 2 3
## 2 CR CR 1.7773 ap100 control-fmri male NA 3 2 3
## 3 H HI_Hi 1.6804 ap100 control-fmri male NA 3 2 3
## 4 H HO_Hi 1.7268 ap100 control-fmri male NA 3 2 3
## 5 H HO_Hi 1.3734 ap100 control-fmri male NA 3 2 3
## 6 H HO_Hi 2.1476 ap100 control-fmri male NA 3 2 3
## stressed life_stress sleep net_neg neg pos negative positive cond_orig
## 1 3 4 4.5 1 3 2.5 3 2.5 TO_4
## 2 3 4 4.5 1 3 2.5 3 2.5 F_0
## 3 3 4 4.5 1 3 2.5 3 2.5 TI_2
## 4 3 4 4.5 1 3 2.5 3 2.5 TO_2
## 5 3 4 4.5 1 3 2.5 3 2.5 TO_2
## 6 3 4 4.5 1 3 2.5 3 2.5 TO_4
## cond reps conf stress_group modality
## 1 outdoor 4 Hi control fmri
## 2 foil 0 N control fmri
## 3 indoor 2 Hi control fmri
## 4 outdoor 2 Hi control fmri
## 5 outdoor 2 Hi control fmri
## 6 outdoor 4 Hi control fmriwith(dtest, table(acc, cond, resp))## , , resp = foil
##
## cond
## acc foil indoor outdoor
## CR 2475 0 0
## FA 0 0 0
## H 0 0 0
## M 0 575 534
## no response 0 0 0
## SM 0 0 0
##
## , , resp = indoor
##
## cond
## acc foil indoor outdoor
## CR 0 0 0
## FA 715 0 0
## H 0 2220 0
## M 0 0 0
## no response 0 0 0
## SM 0 0 981
##
## , , resp = no response
##
## cond
## acc foil indoor outdoor
## CR 0 0 0
## FA 0 0 0
## H 0 0 0
## M 0 0 0
## no response 51 45 44
## SM 0 0 0
##
## , , resp = outdoor
##
## cond
## acc foil indoor outdoor
## CR 0 0 0
## FA 611 0 0
## H 0 0 2253
## M 0 0 0
## no response 0 0 0
## SM 0 1004 0# Some processing to label HC associative hits, and objectively old/new status
dtest = dtest %>%
mutate(group = stress_group) %>%
dplyr::select(subid, group, run, trial, cond,
shockCond, resp, acc, respRT, reps, conf) %>%
mutate(subj_status = ifelse(resp == 'foil',
'new',
ifelse(resp %in% c('indoor', 'outdoor'),
'old',
'no response')),
acc_num = ifelse(acc %in% c('H', 'CR'), 1, 0),
acc_SH_num = ifelse(acc %in% c('H', 'CR'), 1, 0),
acc_SH_num = replace(acc_SH_num, conf=='Lo', 0),
obj_status = ifelse(cond == 'foil', 'new', 'old'))
# str(dtest)
head(dtest)## subid group run trial cond shockCond resp acc respRT reps conf
## 1 ap100 control 1 1 outdoor safe indoor SM 2.1094 4 Hi
## 2 ap100 control 1 2 foil safe foil CR 1.7773 0 N
## 3 ap100 control 1 3 indoor safe indoor H 1.6804 2 Hi
## 4 ap100 control 1 4 outdoor safe outdoor H 1.7268 2 Hi
## 5 ap100 control 1 5 outdoor safe outdoor H 1.3734 2 Hi
## 6 ap100 control 1 6 outdoor safe outdoor H 2.1476 4 Hi
## subj_status acc_num acc_SH_num obj_status
## 1 old 0 0 old
## 2 new 1 1 new
## 3 old 1 1 old
## 4 old 1 1 old
## 5 old 1 1 old
## 6 old 1 1 olddtest %>% filter(subid == 'ap160', run == 5, trial == 13) # make sure tossed (2 shocks in a row, so shock_and_post == 2)## [1] subid group run trial cond
## [6] shockCond resp acc respRT reps
## [11] conf subj_status acc_num acc_SH_num obj_status
## <0 rows> (or 0-length row.names)with(dtest, table(acc_num, conf))## conf
## acc_num Hi Lo N
## 0 922 2389 1249
## 1 3025 1448 2475with(dtest, table(acc_SH_num, conf, acc))## , , acc = CR
##
## conf
## acc_SH_num Hi Lo N
## 0 0 0 0
## 1 0 0 2475
##
## , , acc = FA
##
## conf
## acc_SH_num Hi Lo N
## 0 216 1110 0
## 1 0 0 0
##
## , , acc = H
##
## conf
## acc_SH_num Hi Lo N
## 0 0 1448 0
## 1 3025 0 0
##
## , , acc = M
##
## conf
## acc_SH_num Hi Lo N
## 0 0 0 1109
## 1 0 0 0
##
## , , acc = no response
##
## conf
## acc_SH_num Hi Lo N
## 0 0 0 140
## 1 0 0 0
##
## , , acc = SM
##
## conf
## acc_SH_num Hi Lo N
## 0 706 1279 0
## 1 0 0 0str(dtest)## 'data.frame': 11508 obs. of 15 variables:
## $ subid : Factor w/ 47 levels "ap100","ap101",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## $ run : int 1 1 1 1 1 1 1 1 1 1 ...
## $ trial : int 1 2 3 4 5 6 7 8 9 10 ...
## $ cond : Factor w/ 3 levels "foil","indoor",..: 3 1 2 3 3 3 1 2 2 1 ...
## $ shockCond : Factor w/ 2 levels "safe","threat": 1 1 1 1 1 1 1 1 1 1 ...
## $ resp : Factor w/ 4 levels "foil","indoor",..: 2 1 2 4 4 4 1 1 1 1 ...
## $ acc : Factor w/ 6 levels "CR","FA","H",..: 6 1 3 3 3 3 1 4 4 1 ...
## $ respRT : num 2.11 1.78 1.68 1.73 1.37 ...
## $ reps : int 4 0 2 2 2 4 0 4 2 0 ...
## $ conf : Factor w/ 3 levels "Hi","Lo","N": 1 3 1 1 1 1 3 3 3 3 ...
## $ subj_status: chr "old" "new" "old" "old" ...
## $ acc_num : num 0 1 1 1 1 1 1 0 0 1 ...
## $ acc_SH_num : num 0 1 1 1 1 1 1 0 0 1 ...
## $ obj_status : chr "old" "new" "old" "old" ...1a) Recognition: dprime
Calculate dprime
# get obj counts
obj_counts = dtest %>% group_by(subid, group, reps, shockCond, obj_status) %>%
summarise(count = n()) %>%
rename(trial_type = obj_status) %>%
ungroup()
unique(dtest$acc)## [1] SM CR H M FA no response
## Levels: CR FA H M no response SM# get counts by bin
coded_counts = dtest %>%
filter(acc %in% c('H', 'SM', 'FA')) %>%
mutate(acc = ifelse(acc == 'FA', 'fa', 'hit')) %>%
group_by(subid, group, reps, shockCond, acc) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), shockCond, nesting(reps, acc),
fill = list(count = 0))
# combine hits and olds
d_old = coded_counts %>% filter(acc == 'hit') %>%
dplyr::select(-acc) %>%
rename(hits = count) %>%
right_join(obj_counts %>%
filter(trial_type == 'old') %>%
dplyr::select(-trial_type) %>%
rename(olds = count))## Joining, by = c("subid", "group", "shockCond", "reps")# combine fas and news
d_new= coded_counts %>% filter(acc == 'fa') %>%
dplyr::select(-acc) %>%
rename(fas = count) %>%
right_join(obj_counts %>%
filter(trial_type == 'new') %>%
dplyr::select(-trial_type) %>%
rename(news = count)) %>%
dplyr::select(-reps)## Joining, by = c("subid", "group", "shockCond", "reps")# compute hit/fa rates, and other
dmerge = d_old %>% left_join(d_new) %>%
mutate(hitRate = hits/olds,
faRate = fas/news,
halfHit = 0.5/olds,
halfFA = 0.5/news,
crRate = 1-faRate,
reps = factor(reps))## Joining, by = c("subid", "group", "shockCond")# get summary stats, before adjusting for 0s/1s
dmerge %>%
group_by(subid, group) %>%
summarise(hitRate = mean(hitRate),
faRate = mean(faRate),
crRate = mean(crRate)) %>%
group_by(group) %>%
summarise_each(funs(mean, std.error, length))## Warning in mean.default(structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, :
## argument is not numeric or logical: returning NA## Warning in mean.default(structure(c(23L, 24L, 25L, 26L, 27L, 28L, 29L,
## 30L, : argument is not numeric or logical: returning NA## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.## # A tibble: 2 x 13
## group subid_mean hitRate_mean faRate_mean crRate_mean subid_std.error
## <fctr> <lgl> <dbl> <dbl> <dbl> <dbl>
## 1 control NA 0.8794949 0.3289757 0.6710243 1.571244
## 2 stress NA 0.8095500 0.3618958 0.6381042 1.524755
## # ... with 7 more variables: hitRate_std.error <dbl>,
## # faRate_std.error <dbl>, crRate_std.error <dbl>, subid_length <int>,
## # hitRate_length <int>, faRate_length <int>, crRate_length <int>dmerge %>%
group_by(subid, group, shockCond) %>%
summarise(hitRate = mean(hitRate),
faRate = mean(faRate),
crRate = mean(crRate)) %>%
group_by(group, shockCond) %>%
summarise_each(funs(mean, std.error, length))## Warning in mean.default(structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, :
## argument is not numeric or logical: returning NA## Warning in mean.default(structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, :
## argument is not numeric or logical: returning NA## Warning in mean.default(structure(c(23L, 24L, 25L, 26L, 27L, 28L, 29L,
## 30L, : argument is not numeric or logical: returning NA
## Warning in mean.default(structure(c(23L, 24L, 25L, 26L, 27L, 28L, 29L,
## 30L, : argument is not numeric or logical: returning NA## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.## Source: local data frame [4 x 14]
## Groups: group [?]
##
## # A tibble: 4 x 14
## group shockCond subid_mean hitRate_mean faRate_mean crRate_mean
## <fctr> <fctr> <lgl> <dbl> <dbl> <dbl>
## 1 control safe NA 0.8763558 0.3303117 0.6696883
## 2 control threat NA 0.8826339 0.3276398 0.6723602
## 3 stress safe NA 0.8146807 0.3660714 0.6339286
## 4 stress threat NA 0.8044194 0.3577201 0.6422799
## # ... with 8 more variables: subid_std.error <dbl>,
## # hitRate_std.error <dbl>, faRate_std.error <dbl>,
## # crRate_std.error <dbl>, subid_length <int>, hitRate_length <int>,
## # faRate_length <int>, crRate_length <int># replace 0 and 1s
dmerge = dmerge %>% mutate(faRate=ifelse(faRate==0, halfFA, faRate),
faRate=ifelse(faRate==1, 1-halfFA, faRate),
hitRate=ifelse(hitRate==0, halfHit, hitRate),
hitRate=ifelse(hitRate==1, 1-halfHit, hitRate))
# calc SDT measures
dmerge = dmerge %>% mutate(dprime = qnorm(hitRate) - qnorm(faRate),
c = -(qnorm(hitRate) + qnorm(faRate))/2) %>%
mutate(reps = factor(reps))
dmerge %>%
group_by(subid, group) %>%
summarise(n()) %>%
group_by(group) %>%
summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 23
## 2 stress 24dmerge %>%
group_by(subid, group) %>%
summarise(dprime = mean(dprime),
c = mean(c)) %>%
group_by(group) %>%
summarise_each(funs(mean, std.error, length))## Warning in mean.default(structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, :
## argument is not numeric or logical: returning NA## Warning in mean.default(structure(c(23L, 24L, 25L, 26L, 27L, 28L, 29L,
## 30L, : argument is not numeric or logical: returning NA## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.## # A tibble: 2 x 10
## group subid_mean dprime_mean c_mean subid_std.error
## <fctr> <lgl> <dbl> <dbl> <dbl>
## 1 control NA 1.894499 -0.3827985 1.571244
## 2 stress NA 1.478735 -0.2995141 1.524755
## # ... with 5 more variables: dprime_std.error <dbl>, c_std.error <dbl>,
## # subid_length <int>, dprime_length <int>, c_length <int>dmerge %>%
group_by(subid, group, shockCond) %>%
summarise(dprime = mean(dprime),
c = mean(c)) %>%
group_by(group, shockCond) %>%
summarise_each(funs(mean, std.error, length))## Warning in mean.default(structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, :
## argument is not numeric or logical: returning NA## Warning in mean.default(structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, :
## argument is not numeric or logical: returning NA## Warning in mean.default(structure(c(23L, 24L, 25L, 26L, 27L, 28L, 29L,
## 30L, : argument is not numeric or logical: returning NA
## Warning in mean.default(structure(c(23L, 24L, 25L, 26L, 27L, 28L, 29L,
## 30L, : argument is not numeric or logical: returning NA## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm): Calling var(x) on a factor x is deprecated and will become an error.
## Use something like 'all(duplicated(x)[-1L])' to test for a constant vector.## Source: local data frame [4 x 11]
## Groups: group [?]
##
## # A tibble: 4 x 11
## group shockCond subid_mean dprime_mean c_mean subid_std.error
## <fctr> <fctr> <lgl> <dbl> <dbl> <dbl>
## 1 control safe NA 1.868164 -0.3664629 1.571244
## 2 control threat NA 1.920834 -0.3991342 1.571244
## 3 stress safe NA 1.493970 -0.3106890 1.524755
## 4 stress threat NA 1.463500 -0.2883392 1.524755
## # ... with 5 more variables: dprime_std.error <dbl>, c_std.error <dbl>,
## # subid_length <int>, dprime_length <int>, c_length <int>item_d = dmergeDprime stats
dmerge %>%
group_by(subid, group) %>%
summarise(n()) %>%
group_by(group) %>%
summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 23
## 2 stress 24contrasts(dmerge$group) = c(1,-1)
contrasts(dmerge$shockCond) = c(1,-1)
contrasts(dmerge$reps) = c(-1,1)
summary(lmer(dprime ~ group * shockCond * reps +
(1 + shockCond + reps| subid), data=dmerge)) #not enough obs for interaction RE## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## dprime ~ group * shockCond * reps + (1 + shockCond + reps | subid)
## Data: dmerge
##
## REML criterion at convergence: 293.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4575 -0.4637 -0.0290 0.4703 1.7739
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.54907 0.7410
## shockCond1 0.03654 0.1912 -0.03
## reps1 0.02114 0.1454 0.35 -0.09
## Residual 0.05439 0.2332
## Number of obs: 188, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.686617 0.109440 45.000000 15.411 < 2e-16
## group1 0.207882 0.109440 45.000000 1.900 0.0639
## shockCond1 -0.005550 0.032669 45.000000 -0.170 0.8659
## reps1 0.207302 0.027192 45.000000 7.624 1.22e-09
## group1:shockCond1 -0.020785 0.032669 45.000000 -0.636 0.5279
## group1:reps1 -0.014628 0.027192 45.000000 -0.538 0.5932
## shockCond1:reps1 0.017423 0.017012 45.000000 1.024 0.3112
## group1:shockCond1:reps1 0.005108 0.017012 45.000000 0.300 0.7654
##
## (Intercept) ***
## group1 .
## shockCond1
## reps1 ***
## group1:shockCond1
## group1:reps1
## shockCond1:reps1
## group1:shockCond1:reps1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 shckC1 reps1 gr1:C1 grp1:1 shC1:1
## group1 0.021
## shockCond1 -0.022 0.000
## reps1 0.268 0.006 -0.062
## grp1:shckC1 0.000 -0.022 0.021 -0.001
## group1:rps1 0.006 0.268 -0.001 0.021 -0.062
## shckCnd1:r1 0.000 0.000 0.000 0.000 0.000 0.000
## grp1:shC1:1 0.000 0.000 0.000 0.000 0.000 0.000 0.021# effect of shockCond within stress:
summary(lmer(dprime ~ shockCond * reps +
(1 + shockCond + reps| subid), data=dmerge %>% filter(group == 'stress')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: dprime ~ shockCond * reps + (1 + shockCond + reps | subid)
## Data: dmerge %>% filter(group == "stress")
##
## REML criterion at convergence: 124.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.61905 -0.45463 0.05425 0.44985 1.73294
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.33629 0.5799
## shockCond1 0.04069 0.2017 0.41
## reps1 0.01088 0.1043 0.68 0.77
## Residual 0.05904 0.2430
## Number of obs: 96, groups: subid, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.47873 0.12094 23.00000 12.227 1.52e-11 ***
## shockCond1 0.01523 0.04807 23.00000 0.317 0.754
## reps1 0.22193 0.03269 23.00000 6.789 6.34e-07 ***
## shockCond1:reps1 0.01232 0.02480 23.00000 0.497 0.624
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) shckC1 reps1
## shockCond1 0.340
## reps1 0.431 0.430
## shckCnd1:r1 0.000 0.000 0.000# stress means by run type
dmerge %>% filter(group == 'stress') %>%
group_by(shockCond) %>%
summarise(dprime = mean(dprime)) ## # A tibble: 2 x 2
## shockCond dprime
## <fctr> <dbl>
## 1 safe 1.49397
## 2 threat 1.46350# effect of group just within safe?
summary(lmer(dprime ~ group * reps +
(1| subid), data=dmerge %>% filter(shockCond == 'safe')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: dprime ~ group * reps + (1 | subid)
## Data: dmerge %>% filter(shockCond == "safe")
##
## REML criterion at convergence: 174.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.05400 -0.44801 0.00997 0.37107 2.13855
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.55945 0.7480
## Residual 0.09165 0.3027
## Number of obs: 94, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.68107 0.11351 45.00000 14.810 < 2e-16 ***
## group1 0.18710 0.11351 45.00000 1.648 0.106
## reps1 0.22473 0.03123 45.00000 7.195 5.24e-09 ***
## group1:reps1 -0.00952 0.03123 45.00000 -0.305 0.762
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 reps1
## group1 0.021
## reps1 0.000 0.000
## group1:rps1 0.000 0.000 0.021Criterion stats
summary(lmer(c ~ group * shockCond * reps +
(1 + shockCond + reps| subid), data=dmerge)) #not enough obs for interaction RE## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: c ~ group * shockCond * reps + (1 + shockCond + reps | subid)
## Data: dmerge
##
## REML criterion at convergence: 105.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.43344 -0.40465 0.00636 0.41143 1.84765
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.272795 0.5223
## shockCond1 0.021279 0.1459 -0.12
## reps1 0.005285 0.0727 -0.25 -0.14
## Residual 0.013596 0.1166
## Number of obs: 188, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.341156 0.076675 45.000000 -4.449 5.61e-05
## group1 -0.041642 0.076675 45.000000 -0.543 0.590
## shockCond1 0.002580 0.022920 45.000000 0.113 0.911
## reps1 -0.103651 0.013596 45.000000 -7.624 1.22e-09
## group1:shockCond1 0.013755 0.022920 45.000000 0.600 0.551
## group1:reps1 0.007314 0.013596 45.000000 0.538 0.593
## shockCond1:reps1 -0.008712 0.008506 45.000000 -1.024 0.311
## group1:shockCond1:reps1 -0.002554 0.008506 45.000000 -0.300 0.765
##
## (Intercept) ***
## group1
## shockCond1
## reps1 ***
## group1:shockCond1
## group1:reps1
## shockCond1:reps1
## group1:shockCond1:reps1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 shckC1 reps1 gr1:C1 grp1:1 shC1:1
## group1 0.021
## shockCond1 -0.114 -0.002
## reps1 -0.192 -0.004 -0.099
## grp1:shckC1 -0.002 -0.114 0.021 -0.002
## group1:rps1 -0.004 -0.192 -0.002 0.021 -0.099
## shckCnd1:r1 0.000 0.000 0.000 0.000 0.000 0.000
## grp1:shC1:1 0.000 0.000 0.000 0.000 0.000 0.000 0.021Does cortisol affect dprime?
d_cort = read.csv('/Volumes/group/awagner/sgagnon/AP/data/cortisol/cort_percentchange_testbaseline.csv')
dprime_cort = dmerge %>% left_join(d_cort)## Joining, by = c("subid", "group")str(dprime_cort)## Classes 'tbl_df', 'tbl' and 'data.frame': 188 obs. of 18 variables:
## $ subid : Factor w/ 47 levels "ap100","ap101",..: 1 1 1 1 2 2 2 2 3 3 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] 1 -1
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr "control" "stress"
## .. .. ..$ : NULL
## $ shockCond : Factor w/ 2 levels "safe","threat": 1 2 1 2 1 2 1 2 1 2 ...
## ..- attr(*, "contrasts")= num [1:2, 1] 1 -1
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr "safe" "threat"
## .. .. ..$ : NULL
## $ reps : Factor w/ 2 levels "2","4": 1 1 2 2 1 1 2 2 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -1 1
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr "2" "4"
## .. .. ..$ : NULL
## $ hits : num 29 32 31 35 28 32 39 34 40 36 ...
## $ olds : int 38 42 38 42 42 42 42 42 42 40 ...
## $ fas : num 8 8 8 8 1 1 1 1 33 28 ...
## $ news : int 37 42 37 42 42 42 42 42 42 42 ...
## $ hitRate : num 0.763 0.762 0.816 0.833 0.667 ...
## $ faRate : num 0.2162 0.1905 0.2162 0.1905 0.0238 ...
## $ halfHit : num 0.0132 0.0119 0.0132 0.0119 0.0119 ...
## $ halfFA : num 0.0135 0.0119 0.0135 0.0119 0.0119 ...
## $ crRate : num 0.784 0.81 0.784 0.81 0.976 ...
## $ dprime : num 1.5 1.59 1.68 1.84 2.41 ...
## $ c : num 0.0343 0.0818 -0.0572 -0.0456 0.775 ...
## $ X : int 0 0 0 0 1 1 1 1 2 2 ...
## $ assay : Factor w/ 2 levels "IBL","Salimetrics": 1 1 1 1 1 1 1 1 1 1 ...
## $ log.ug.dL.: num -7.36 -7.36 -7.36 -7.36 -17.43 ...contrasts(dprime_cort$assay) = c(1,-1)
contrasts(dprime_cort$shockCond) = c(1,-1)
contrasts(dprime_cort$reps) = c(-1,1)
fit = lmer(dprime ~ scale(log.ug.dL.) * shockCond * reps + assay +
(1 + shockCond + reps| subid), data=dprime_cort)
fit2 = lmer(dprime ~ poly(log.ug.dL.,2) * shockCond * reps + assay +
(1 + shockCond + reps| subid), data=dprime_cort)
anova(fit, fit2)## refitting model(s) with ML (instead of REML)## Data: dprime_cort
## Models:
## object: dprime ~ scale(log.ug.dL.) * shockCond * reps + assay + (1 +
## object: shockCond + reps | subid)
## ..1: dprime ~ poly(log.ug.dL., 2) * shockCond * reps + assay + (1 +
## ..1: shockCond + reps | subid)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## object 16 284.59 336.37 -126.29 252.59
## ..1 20 289.49 354.22 -124.75 249.49 3.0955 4 0.542summary(fit)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: dprime ~ scale(log.ug.dL.) * shockCond * reps + assay + (1 +
## shockCond + reps | subid)
## Data: dprime_cort
##
## REML criterion at convergence: 294.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.52112 -0.40713 -0.04724 0.44855 1.85670
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.57565 0.7587
## shockCond1 0.03472 0.1863 -0.11
## reps1 0.02168 0.1472 0.32 -0.06
## Residual 0.05272 0.2296
## Number of obs: 188, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.661958 0.114915 44.410000 14.463
## scale(log.ug.dL.) 0.150183 0.112264 44.130000 1.338
## shockCond1 -0.005108 0.031924 45.000000 -0.160
## reps1 0.207613 0.027235 45.000000 7.623
## assay1 0.086461 0.111175 44.000000 0.778
## scale(log.ug.dL.):shockCond1 0.050873 0.032010 45.000000 1.589
## scale(log.ug.dL.):reps1 -0.009714 0.027307 45.000000 -0.356
## shockCond1:reps1 0.017314 0.016746 45.000000 1.034
## scale(log.ug.dL.):shockCond1:reps1 -0.020646 0.016791 45.000000 -1.230
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## scale(log.ug.dL.) 0.188
## shockCond1 0.874
## reps1 1.23e-09 ***
## assay1 0.441
## scale(log.ug.dL.):shockCond1 0.119
## scale(log.ug.dL.):reps1 0.724
## shockCond1:reps1 0.307
## scale(log.ug.dL.):shockCond1:reps1 0.225
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(..L.) shckC1 reps1 assay1 sc(..L.):C1 s(..L.):1
## scl(lg..L.) -0.006
## shockCond1 -0.089 0.000
## reps1 0.242 0.000 -0.043
## assay1 -0.226 0.025 0.000 0.000
## sc(..L.):C1 0.000 -0.091 0.000 0.000 0.000
## scl(..L.):1 0.000 0.248 0.000 0.000 0.000 -0.043
## shckCnd1:r1 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## s(..L.):C1: 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## shC1:1
## scl(lg..L.)
## shockCond1
## reps1
## assay1
## sc(..L.):C1
## scl(..L.):1
## shckCnd1:r1
## s(..L.):C1: 0.000Just to check, break down dprime also by indoor/outdoor associate
(doesn’t make as much sense here, since collapsing across associates in saying)
# get obj counts
obj_counts = dtest %>% group_by(subid, group, cond, reps, shockCond, obj_status) %>%
summarise(count = n()) %>%
rename(trial_type = obj_status) %>%
ungroup()
# get counts by bin
coded_counts = dtest %>%
filter(acc %in% c('H', 'SM', 'FA')) %>%
mutate(acc = ifelse(acc == 'FA', 'fa', 'hit')) %>%
group_by(subid, group, cond, reps, shockCond, acc) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), shockCond, nesting(cond, reps, acc),
fill = list(count = 0))
# combine hits and olds
d_old = coded_counts %>% filter(acc == 'hit') %>%
dplyr::select(-acc) %>%
rename(hits = count) %>%
right_join(obj_counts %>%
filter(trial_type == 'old') %>%
dplyr::select(-trial_type) %>%
rename(olds = count))## Joining, by = c("subid", "group", "shockCond", "cond", "reps")# combine fas and news
d_new= coded_counts %>% filter(acc == 'fa') %>%
dplyr::select(-acc) %>%
rename(fas = count) %>%
right_join(obj_counts %>%
filter(trial_type == 'new') %>%
dplyr::select(-trial_type) %>%
rename(news = count)) %>%
dplyr::select(-reps, -cond)## Joining, by = c("subid", "group", "shockCond", "cond", "reps")# compute hit/fa rates, and other
dmerge = d_old %>% left_join(d_new) %>%
mutate(hitRate = hits/olds,
faRate = fas/news,
halfHit = 0.5/olds,
halfFA = 0.5/news)## Joining, by = c("subid", "group", "shockCond")dmerge %>% group_by(subid, group, cond) %>%
summarise(hitRate = mean(hitRate), faRate = mean(faRate)) %>%
group_by(group, cond) %>%
summarise_each(funs(mean)) %>%
mutate(crRate = 1-faRate)## Warning in mean.default(structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, :
## argument is not numeric or logical: returning NA
## Warning in mean.default(structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, :
## argument is not numeric or logical: returning NA## Warning in mean.default(structure(c(23L, 24L, 25L, 26L, 27L, 28L, 29L,
## 30L, : argument is not numeric or logical: returning NA
## Warning in mean.default(structure(c(23L, 24L, 25L, 26L, 27L, 28L, 29L,
## 30L, : argument is not numeric or logical: returning NA## Source: local data frame [4 x 6]
## Groups: group [2]
##
## # A tibble: 4 x 6
## group cond subid hitRate faRate crRate
## <fctr> <fctr> <lgl> <dbl> <dbl> <dbl>
## 1 control indoor NA 0.8748706 0.3289757 0.6710243
## 2 control outdoor NA 0.8843313 0.3289757 0.6710243
## 3 stress indoor NA 0.8043045 0.3618958 0.6381042
## 4 stress outdoor NA 0.8148222 0.3618958 0.6381042# replace 0 and 1s
dmerge = dmerge %>% mutate(faRate=ifelse(faRate==0, halfFA, faRate),
faRate=ifelse(faRate==1, 1-halfFA, faRate),
hitRate=ifelse(hitRate==0, halfHit, hitRate),
hitRate=ifelse(hitRate==1, 1-halfHit, hitRate))
# calc SDT measures
dmerge = dmerge %>% mutate(dprime = qnorm(hitRate) - qnorm(faRate),
c = -(qnorm(hitRate) + qnorm(faRate))/2) %>%
mutate(reps = factor(reps),
cond = factor(cond))
dmerge## # A tibble: 376 x 15
## subid group shockCond cond reps hits olds fas news
## <fctr> <fctr> <fctr> <fctr> <fctr> <dbl> <int> <dbl> <int>
## 1 ap100 control safe indoor 2 15 20 8 37
## 2 ap100 control threat indoor 2 16 21 8 42
## 3 ap100 control safe indoor 4 15 18 8 37
## 4 ap100 control threat indoor 4 16 21 8 42
## 5 ap100 control safe outdoor 2 14 18 8 37
## 6 ap100 control threat outdoor 2 16 21 8 42
## 7 ap100 control safe outdoor 4 16 20 8 37
## 8 ap100 control threat outdoor 4 19 21 8 42
## 9 ap101 control safe indoor 2 13 21 1 42
## 10 ap101 control threat indoor 2 15 21 1 42
## # ... with 366 more rows, and 6 more variables: hitRate <dbl>,
## # faRate <dbl>, halfHit <dbl>, halfFA <dbl>, dprime <dbl>, c <dbl>contrasts(dmerge$group) = c(1,-1)
contrasts(dmerge$shockCond) = c(1,-1)
contrasts(dmerge$reps) = c(-1,1)
contrasts(dmerge$cond) = c(-1,1)
summary(lmer(dprime ~ group * shockCond * reps + cond +
(1 + shockCond + reps + cond| subid), data=dmerge),
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=5e5))) # failed to converge w/diff optimizers/increasing iters## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## dprime ~ group * shockCond * reps + cond + (1 + shockCond + reps +
## cond | subid)
## Data: dmerge
##
## REML criterion at convergence: 475.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.25446 -0.59296 -0.02828 0.52060 2.43531
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.544578 0.73796
## shockCond1 0.037970 0.19486 -0.06
## reps1 0.013150 0.11467 0.28 0.03
## cond1 0.004991 0.07065 -0.14 -0.10 -0.55
## Residual 0.081633 0.28571
## Number of obs: 376, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.663e+00 1.087e-01 4.501e+01 15.307 < 2e-16
## group1 1.905e-01 1.083e-01 4.500e+01 1.759 0.0853
## shockCond1 -1.074e-04 3.202e-02 4.500e+01 -0.003 0.9973
## reps1 1.837e-01 2.230e-02 4.510e+01 8.240 1.52e-10
## cond1 2.529e-02 1.798e-02 4.600e+01 1.406 0.1663
## group1:shockCond1 -1.305e-02 3.198e-02 4.500e+01 -0.408 0.6852
## group1:reps1 -2.178e-02 2.167e-02 4.500e+01 -1.005 0.3202
## shockCond1:reps1 1.889e-02 1.474e-02 1.860e+02 1.282 0.2014
## group1:shockCond1:reps1 9.647e-03 1.474e-02 1.860e+02 0.655 0.5135
##
## (Intercept) ***
## group1 .
## shockCond1
## reps1 ***
## cond1
## group1:shockCond1
## group1:reps1
## shockCond1:reps1
## group1:shockCond1:reps1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 shckC1 reps1 cond1 gr1:C1 grp1:1 shC1:1
## group1 0.021
## shockCond1 -0.049 -0.001
## reps1 0.212 0.004 0.021
## cond1 -0.081 0.000 -0.051 -0.236
## grp1:shckC1 -0.001 -0.054 0.021 0.000 0.000
## group1:rps1 0.004 0.199 0.000 0.021 0.000 0.009
## shckCnd1:r1 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## grp1:shC1:1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.021# double check that no group:cond interaction
summary(lmer(dprime ~ group * shockCond * reps + cond + group:cond +
(1 + shockCond + reps + cond| subid), data=dmerge),
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=5e5))) # failed to converge w/diff optimizers/increasing iters## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: dprime ~ group * shockCond * reps + cond + group:cond + (1 +
## shockCond + reps + cond | subid)
## Data: dmerge
##
## REML criterion at convergence: 481.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.23409 -0.58711 -0.02702 0.53125 2.41493
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.544649 0.73800
## shockCond1 0.037973 0.19487 -0.06
## reps1 0.013175 0.11478 0.29 0.03
## cond1 0.005292 0.07274 -0.14 -0.10 -0.54
## Residual 0.081633 0.28571
## Number of obs: 376, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.663e+00 1.087e-01 4.500e+01 15.307 < 2e-16
## group1 1.935e-01 1.087e-01 4.500e+01 1.780 0.0818
## shockCond1 -9.589e-05 3.202e-02 4.500e+01 -0.003 0.9976
## reps1 1.838e-01 2.231e-02 4.500e+01 8.237 1.56e-10
## cond1 2.516e-02 1.816e-02 4.500e+01 1.385 0.1727
## group1:shockCond1 -1.251e-02 3.202e-02 4.500e+01 -0.391 0.6979
## group1:reps1 -2.003e-02 2.231e-02 4.500e+01 -0.898 0.3739
## shockCond1:reps1 1.889e-02 1.474e-02 1.860e+02 1.282 0.2014
## group1:cond1 -5.981e-03 1.816e-02 4.500e+01 -0.329 0.7434
## group1:shockCond1:reps1 9.647e-03 1.474e-02 1.860e+02 0.655 0.5135
##
## (Intercept) ***
## group1 .
## shockCond1
## reps1 ***
## cond1
## group1:shockCond1
## group1:reps1
## shockCond1:reps1
## group1:cond1
## group1:shockCond1:reps1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 shckC1 reps1 cond1 gr1:C1 grp1:r1 shC1:1
## group1 0.021
## shockCond1 -0.049 -0.001
## reps1 0.212 0.005 0.021
## cond1 -0.082 -0.002 -0.051 -0.238
## grp1:shckC1 -0.001 -0.049 0.021 0.000 -0.001
## group1:rps1 0.005 0.212 0.000 0.021 -0.005 0.021
## shckCnd1:r1 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## group1:cnd1 -0.002 -0.082 -0.001 -0.005 0.021 -0.051 -0.238 0.000
## grp1:shC1:1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.021
## grp1:c1
## group1
## shockCond1
## reps1
## cond1
## grp1:shckC1
## group1:rps1
## shckCnd1:r1
## group1:cnd1
## grp1:shC1:1 0.0001b) HC associative dprime
Calculate HC associative dprimes/criterion
# get obj counts
obj_counts = dtest %>% filter(cond %in% c('indoor',
'outdoor')) %>%
group_by(subid, group, reps,
cond, shockCond, obj_status) %>%
summarise(count = n()) %>%
rename(trial_type = obj_status) %>%
ungroup() %>%
complete(nesting(subid, group), shockCond, nesting(reps, cond, trial_type),
fill = list(count = 0))
obj_counts## # A tibble: 376 x 7
## subid group shockCond reps cond trial_type count
## <fctr> <fctr> <fctr> <int> <fctr> <chr> <dbl>
## 1 ap100 control safe 2 indoor old 20
## 2 ap100 control safe 2 outdoor old 18
## 3 ap100 control safe 4 indoor old 18
## 4 ap100 control safe 4 outdoor old 20
## 5 ap100 control threat 2 indoor old 21
## 6 ap100 control threat 2 outdoor old 21
## 7 ap100 control threat 4 indoor old 21
## 8 ap100 control threat 4 outdoor old 21
## 9 ap101 control safe 2 indoor old 21
## 10 ap101 control safe 2 outdoor old 21
## # ... with 366 more rows# write.csv(obj_counts, '~/Desktop/obj_counts.csv')
# get counts for hi conf assoc hits and misses
coded_counts = dtest %>%
filter(acc %in% c('H', 'SM'),
conf == 'Hi') %>%
group_by(subid, group, reps, shockCond, resp, cond, acc) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), shockCond, nesting(reps, acc, cond, resp),
fill = list(count = 0))
coded_counts## # A tibble: 752 x 8
## subid group shockCond reps acc cond resp count
## <fctr> <fctr> <fctr> <int> <fctr> <fctr> <fctr> <dbl>
## 1 ap100 control safe 2 H indoor indoor 10
## 2 ap100 control safe 2 H outdoor outdoor 10
## 3 ap100 control safe 2 SM indoor outdoor 0
## 4 ap100 control safe 2 SM outdoor indoor 1
## 5 ap100 control safe 4 H indoor indoor 12
## 6 ap100 control safe 4 H outdoor outdoor 14
## 7 ap100 control safe 4 SM indoor outdoor 3
## 8 ap100 control safe 4 SM outdoor indoor 1
## 9 ap100 control threat 2 H indoor indoor 10
## 10 ap100 control threat 2 H outdoor outdoor 10
## # ... with 742 more rows# write.csv(coded_counts, '~/Desktop/coded_counts.csv')
# combine hits and olds (on condition)
hit_rates = coded_counts %>%
filter(acc == 'H') %>%
rename(resp_rate = count) %>%
right_join(obj_counts %>%
filter(trial_type == 'old') %>%
dplyr::select(-trial_type) %>%
rename(olds = count)) %>%
mutate(H = resp_rate/olds, half_rate = 0.5/olds,
H=ifelse(H==0, half_rate, H),
H=ifelse(H==1, 1-half_rate, H)) %>%
dplyr::select(-resp_rate, -olds, -half_rate, -cond, -acc)## Joining, by = c("subid", "group", "shockCond", "reps", "cond")hit_rates## # A tibble: 376 x 6
## subid group shockCond reps resp H
## <fctr> <fctr> <fctr> <int> <fctr> <dbl>
## 1 ap100 control safe 2 indoor 0.5000000
## 2 ap100 control safe 2 outdoor 0.5555556
## 3 ap100 control safe 4 indoor 0.6666667
## 4 ap100 control safe 4 outdoor 0.7000000
## 5 ap100 control threat 2 indoor 0.4761905
## 6 ap100 control threat 2 outdoor 0.4761905
## 7 ap100 control threat 4 indoor 0.4761905
## 8 ap100 control threat 4 outdoor 0.6190476
## 9 ap101 control safe 2 indoor 0.4285714
## 10 ap101 control safe 2 outdoor 0.4285714
## # ... with 366 more rows# combine SHs (response) and olds (condition)
sm_rates = coded_counts %>%
filter(acc == 'SM') %>%
rename(resp_rate = count) %>%
right_join(obj_counts %>%
filter(trial_type == 'old') %>%
dplyr::select(-trial_type) %>%
rename(olds = count)) %>%
mutate(SM = resp_rate/olds, half_rate = 0.5/olds,
SM=ifelse(SM==0, half_rate, SM),
SM=ifelse(SM==1, 1-half_rate, SM)) %>%
dplyr::select(-resp_rate, -olds, -half_rate, -cond, -acc)## Joining, by = c("subid", "group", "shockCond", "reps", "cond")sm_rates## # A tibble: 376 x 6
## subid group shockCond reps resp SM
## <fctr> <fctr> <fctr> <int> <fctr> <dbl>
## 1 ap100 control safe 2 outdoor 0.02500000
## 2 ap100 control safe 2 indoor 0.05555556
## 3 ap100 control safe 4 outdoor 0.16666667
## 4 ap100 control safe 4 indoor 0.05000000
## 5 ap100 control threat 2 outdoor 0.09523810
## 6 ap100 control threat 2 indoor 0.04761905
## 7 ap100 control threat 4 outdoor 0.09523810
## 8 ap100 control threat 4 indoor 0.14285714
## 9 ap101 control safe 2 outdoor 0.09523810
## 10 ap101 control safe 2 indoor 0.04761905
## # ... with 366 more rows# join on response (e.g., "indoor" hit to indoor - fa "indoor"" to outdoor)
dmerge = hit_rates %>% left_join(sm_rates)## Joining, by = c("subid", "group", "shockCond", "reps", "resp")dmerge## # A tibble: 376 x 7
## subid group shockCond reps resp H SM
## <fctr> <fctr> <fctr> <int> <fctr> <dbl> <dbl>
## 1 ap100 control safe 2 indoor 0.5000000 0.05555556
## 2 ap100 control safe 2 outdoor 0.5555556 0.02500000
## 3 ap100 control safe 4 indoor 0.6666667 0.05000000
## 4 ap100 control safe 4 outdoor 0.7000000 0.16666667
## 5 ap100 control threat 2 indoor 0.4761905 0.04761905
## 6 ap100 control threat 2 outdoor 0.4761905 0.09523810
## 7 ap100 control threat 4 indoor 0.4761905 0.14285714
## 8 ap100 control threat 4 outdoor 0.6190476 0.09523810
## 9 ap101 control safe 2 indoor 0.4285714 0.04761905
## 10 ap101 control safe 2 outdoor 0.4285714 0.09523810
## # ... with 366 more rows# calc SDT measures
dmerge = dmerge %>% mutate(dprime = qnorm(H) - qnorm(SM),
c = -(qnorm(H) + qnorm(SM))/2) %>%
mutate(reps = factor(reps),
resp = factor(resp))
dmerge## # A tibble: 376 x 9
## subid group shockCond reps resp H SM dprime
## <fctr> <fctr> <fctr> <fctr> <fctr> <dbl> <dbl> <dbl>
## 1 ap100 control safe 2 indoor 0.5000000 0.05555556 1.593219
## 2 ap100 control safe 2 outdoor 0.5555556 0.02500000 2.099674
## 3 ap100 control safe 4 indoor 0.6666667 0.05000000 2.075581
## 4 ap100 control safe 4 outdoor 0.7000000 0.16666667 1.491822
## 5 ap100 control threat 2 indoor 0.4761905 0.04761905 1.608674
## 6 ap100 control threat 2 outdoor 0.4761905 0.09523810 1.249455
## 7 ap100 control threat 4 indoor 0.4761905 0.14285714 1.007853
## 8 ap100 control threat 4 outdoor 0.6190476 0.09523810 1.612152
## 9 ap101 control safe 2 indoor 0.4285714 0.04761905 1.488379
## 10 ap101 control safe 2 outdoor 0.4285714 0.09523810 1.129159
## # ... with 366 more rows, and 1 more variables: c <dbl>dmerge %>% group_by(subid, group) %>%
summarise(hitRate=mean(H),
smRate=mean(SM),
dprime=mean(dprime),
c = mean(c)) %>%
group_by(group) %>%
summarise_each(funs(mean))## Warning in mean.default(structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, :
## argument is not numeric or logical: returning NA## Warning in mean.default(structure(c(23L, 24L, 25L, 26L, 27L, 28L, 29L,
## 30L, : argument is not numeric or logical: returning NA## # A tibble: 2 x 6
## group subid hitRate smRate dprime c
## <fctr> <lgl> <dbl> <dbl> <dbl> <dbl>
## 1 control NA 0.4893292 0.10009728 1.3663338 0.731530
## 2 stress NA 0.3043761 0.09622391 0.8003808 1.038548assoc_d = dmerge
assoc_d## # A tibble: 376 x 9
## subid group shockCond reps resp H SM dprime
## <fctr> <fctr> <fctr> <fctr> <fctr> <dbl> <dbl> <dbl>
## 1 ap100 control safe 2 indoor 0.5000000 0.05555556 1.593219
## 2 ap100 control safe 2 outdoor 0.5555556 0.02500000 2.099674
## 3 ap100 control safe 4 indoor 0.6666667 0.05000000 2.075581
## 4 ap100 control safe 4 outdoor 0.7000000 0.16666667 1.491822
## 5 ap100 control threat 2 indoor 0.4761905 0.04761905 1.608674
## 6 ap100 control threat 2 outdoor 0.4761905 0.09523810 1.249455
## 7 ap100 control threat 4 indoor 0.4761905 0.14285714 1.007853
## 8 ap100 control threat 4 outdoor 0.6190476 0.09523810 1.612152
## 9 ap101 control safe 2 indoor 0.4285714 0.04761905 1.488379
## 10 ap101 control safe 2 outdoor 0.4285714 0.09523810 1.129159
## # ... with 366 more rows, and 1 more variables: c <dbl>HC assoc dprime stats
contrasts(dmerge$group) = c(1,-1)
contrasts(dmerge$shockCond) = c(1,-1)
contrasts(dmerge$reps) = c(-1,1)
contrasts(dmerge$resp) = c(-1,1); contrasts(dmerge$resp)## [,1]
## indoor -1
## outdoor 1# hist(dmerge$dprime)
# dmerge
dmerge %>% group_by(subid, group) %>% summarise(n()) %>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 23
## 2 stress 24# include group interactions by resp, shockcond, reps
summary(lmer(dprime ~ group * (shockCond * reps + resp) +
(1 + reps + shockCond + resp| subid), data=dmerge,
control=lmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## dprime ~ group * (shockCond * reps + resp) + (1 + reps + shockCond +
## resp | subid)
## Data: dmerge
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 618.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.89600 -0.65280 -0.09113 0.61185 2.51073
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.4802490 0.69300
## reps1 0.0140203 0.11841 0.58
## shockCond1 0.0090010 0.09487 0.33 0.32
## resp1 0.0006083 0.02466 0.26 0.67 0.55
## Residual 0.1669692 0.40862
## Number of obs: 376, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.083357 0.103281 45.000000 10.489 1.14e-13
## group1 0.282976 0.103281 45.000000 2.740 0.00878
## shockCond1 -0.017786 0.025216 45.000000 -0.705 0.48424
## reps1 0.210234 0.027253 45.000000 7.714 9.01e-10
## resp1 0.039831 0.021383 45.000000 1.863 0.06903
## shockCond1:reps1 0.050742 0.021078 186.000000 2.407 0.01704
## group1:shockCond1 0.024053 0.025216 45.000000 0.954 0.34525
## group1:reps1 0.058947 0.027253 45.000000 2.163 0.03589
## group1:resp1 -0.020491 0.021383 45.000000 -0.958 0.34303
## group1:shockCond1:reps1 0.008746 0.021078 186.000000 0.415 0.67867
##
## (Intercept) ***
## group1 **
## shockCond1
## reps1 ***
## resp1 .
## shockCond1:reps1 *
## group1:shockCond1
## group1:reps1 *
## group1:resp1
## group1:shockCond1:reps1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 shckC1 reps1 resp1 shC1:1 gr1:C1 grp1:rp1
## group1 0.021
## shockCond1 0.179 0.004
## reps1 0.358 0.008 0.111
## resp1 0.044 0.001 0.051 0.071
## shckCnd1:r1 0.000 0.000 0.000 0.000 0.000
## grp1:shckC1 0.004 0.179 0.021 0.002 0.001 0.000
## group1:rps1 0.008 0.358 0.002 0.021 0.002 0.000 0.111
## group1:rsp1 0.001 0.044 0.001 0.002 0.021 0.000 0.051 0.071
## grp1:shC1:1 0.000 0.000 0.000 0.000 0.000 0.021 0.000 0.000
## grp1:rs1
## group1
## shockCond1
## reps1
## resp1
## shckCnd1:r1
## grp1:shckC1
## group1:rps1
## group1:rsp1
## grp1:shC1:1 0.000# does effect hold looking at safe block?
summary(lmer(dprime ~ group * (reps + resp) +
(1 + reps + resp| subid), data=dmerge %>% filter(shockCond == 'safe'),
control=lmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: dprime ~ group * (reps + resp) + (1 + reps + resp | subid)
## Data: dmerge %>% filter(shockCond == "safe")
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 343.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.51099 -0.53218 -0.00686 0.52236 2.17675
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.546522 0.73927
## reps1 0.055751 0.23612 0.30
## resp1 0.007481 0.08649 0.15 -0.05
## Residual 0.112738 0.33576
## Number of obs: 188, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.06557 0.11060 45.00000 9.634 1.65e-12 ***
## group1 0.30703 0.11060 45.00000 2.776 0.00799 **
## reps1 0.26098 0.04227 45.00000 6.174 1.72e-07 ***
## resp1 0.02350 0.02755 45.00000 0.853 0.39827
## group1:reps1 0.06769 0.04227 45.00000 1.601 0.11627
## group1:resp1 -0.03397 0.02755 45.00000 -1.233 0.22407
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 reps1 resp1 grp1:rp1
## group1 0.021
## reps1 0.236 0.005
## resp1 0.066 0.001 -0.017
## group1:rps1 0.005 0.236 0.021 0.000
## group1:rsp1 0.001 0.066 0.000 0.021 -0.017# is effect of group diff for 2 vs 4 reps?
summary(lmer(dprime ~ group * (shockCond + resp) +
(1 + shockCond + resp| subid), data=dmerge %>% filter(reps == 2),
control=lmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: dprime ~ group * (shockCond + resp) + (1 + shockCond + resp |
## subid)
## Data: dmerge %>% filter(reps == 2)
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 320.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.96727 -0.49930 -0.01663 0.45363 2.07175
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.41927 0.6475
## shockCond1 0.04727 0.2174 0.16
## resp1 0.02063 0.1436 -0.09 -0.33
## Residual 0.08871 0.2978
## Number of obs: 188, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.87312 0.09694 45.00000 9.007 1.24e-11 ***
## group1 0.22403 0.09694 45.00000 2.311 0.0255 *
## shockCond1 -0.06853 0.03845 45.00000 -1.782 0.0814 .
## resp1 0.04880 0.03019 45.00000 1.617 0.1129
## group1:shockCond1 0.01531 0.03845 45.00000 0.398 0.6924
## group1:resp1 -0.01551 0.03019 45.00000 -0.514 0.6100
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 shckC1 resp1 gr1:C1
## group1 0.021
## shockCond1 0.126 0.003
## resp1 -0.058 -0.001 -0.188
## grp1:shckC1 0.003 0.126 0.021 -0.004
## group1:rsp1 -0.001 -0.058 -0.004 0.021 -0.188summary(lmer(dprime ~ group * (shockCond + resp) +
(1 + shockCond + resp| subid), data=dmerge %>% filter(reps == 4),
control=lmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: dprime ~ group * (shockCond + resp) + (1 + shockCond + resp |
## subid)
## Data: dmerge %>% filter(reps == 4)
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 344.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.51451 -0.52416 -0.03364 0.54392 1.77758
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.60208 0.7759
## shockCond1 0.04000 0.2000 0.20
## resp1 0.01285 0.1134 0.20 0.50
## Residual 0.11398 0.3376
## Number of obs: 188, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.29359 0.11586 45.00000 11.166 1.47e-14 ***
## group1 0.34192 0.11586 45.00000 2.951 0.00501 **
## shockCond1 0.03296 0.03818 45.00000 0.863 0.39267
## resp1 0.03086 0.02967 45.00000 1.040 0.30380
## group1:shockCond1 0.03280 0.03818 45.00000 0.859 0.39493
## group1:resp1 -0.02548 0.02967 45.00000 -0.859 0.39504
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 shckC1 resp1 gr1:C1
## group1 0.021
## shockCond1 0.152 0.003
## resp1 0.108 0.002 0.214
## grp1:shckC1 0.003 0.152 0.021 0.005
## group1:rsp1 0.002 0.108 0.005 0.021 0.214# check out group x reps interaction + shockCond just within stress group
summary(lmer(dprime ~ shockCond * reps + resp +
(1 + reps * shockCond + resp| subid),
data=dmerge %>% filter(group == 'stress'),
control=lmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## dprime ~ shockCond * reps + resp + (1 + reps * shockCond + resp |
## subid)
## Data: dmerge %>% filter(group == "stress")
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 304.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.38556 -0.62450 -0.08574 0.47480 2.20730
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.357979 0.59831
## reps1 0.005456 0.07386 0.86
## shockCond1 0.013854 0.11770 0.37 0.21
## resp1 0.003892 0.06239 -0.41 -0.65 0.59
## reps1:shockCond1 0.014490 0.12037 0.43 0.42 0.56 0.21
## Residual 0.157827 0.39727
## Number of obs: 192, groups: subid, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.80038 0.12545 23.00000 6.380 1.64e-06 ***
## shockCond1 -0.04184 0.03741 23.47000 -1.118 0.2747
## reps1 0.15129 0.03239 30.53000 4.670 5.69e-05 ***
## resp1 0.06032 0.03137 36.28000 1.923 0.0624 .
## shockCond1:reps1 0.04200 0.03776 23.03000 1.112 0.2775
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) shckC1 reps1 resp1
## shockCond1 0.230
## reps1 0.391 0.063
## resp1 -0.161 0.155 -0.122
## shckCnd1:r1 0.274 0.234 0.126 0.054# simple effects to double check:
# control
contrasts(dmerge$group) = c(0,1); contrasts(dmerge$group)## [,1]
## control 0
## stress 1summary(lmer(dprime ~ group * (shockCond * reps + resp) +
(1 + reps + shockCond + resp| subid), data=dmerge,
control=lmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## dprime ~ group * (shockCond * reps + resp) + (1 + reps + shockCond +
## resp | subid)
## Data: dmerge
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 611.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.89600 -0.65280 -0.09113 0.61185 2.51073
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.4802498 0.69300
## reps1 0.0140203 0.11841 0.58
## shockCond1 0.0090010 0.09487 0.33 0.32
## resp1 0.0006083 0.02466 0.26 0.67 0.55
## Residual 0.1669692 0.40862
## Number of obs: 376, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.366334 0.147607 45.000000 9.257 5.52e-12
## group1 -0.565953 0.206562 45.000000 -2.740 0.00878
## shockCond1 0.006267 0.036039 45.000000 0.174 0.86273
## reps1 0.269181 0.038949 45.000000 6.911 1.38e-08
## resp1 0.019340 0.030560 45.000000 0.633 0.53003
## shockCond1:reps1 0.059488 0.030124 186.000000 1.975 0.04977
## group1:shockCond1 -0.048105 0.050433 45.000000 -0.954 0.34525
## group1:reps1 -0.117895 0.054505 45.000000 -2.163 0.03589
## group1:resp1 0.040982 0.042765 45.000000 0.958 0.34303
## group1:shockCond1:reps1 -0.017492 0.042155 186.000000 -0.415 0.67867
##
## (Intercept) ***
## group1 **
## shockCond1
## reps1 ***
## resp1
## shockCond1:reps1 *
## group1:shockCond1
## group1:reps1 *
## group1:resp1
## group1:shockCond1:reps1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 shckC1 reps1 resp1 shC1:1 gr1:C1 grp1:rp1
## group1 -0.715
## shockCond1 0.179 -0.128
## reps1 0.358 -0.256 0.111
## resp1 0.044 -0.031 0.051 0.071
## shckCnd1:r1 0.000 0.000 0.000 0.000 0.000
## grp1:shckC1 -0.128 0.179 -0.715 -0.079 -0.036 0.000
## group1:rps1 -0.256 0.358 -0.079 -0.715 -0.051 0.000 0.111
## group1:rsp1 -0.031 0.044 -0.036 -0.051 -0.715 0.000 0.051 0.071
## grp1:shC1:1 0.000 0.000 0.000 0.000 0.000 -0.715 0.000 0.000
## grp1:rs1
## group1
## shockCond1
## reps1
## resp1
## shckCnd1:r1
## grp1:shckC1
## group1:rps1
## group1:rsp1
## grp1:shC1:1 0.000# stress
contrasts(dmerge$group) = c(1,0); contrasts(dmerge$group)## [,1]
## control 1
## stress 0summary(lmer(dprime ~ group * (shockCond * reps + resp) +
(1 + reps + shockCond + resp| subid), data=dmerge,
control=lmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## dprime ~ group * (shockCond * reps + resp) + (1 + reps + shockCond +
## resp | subid)
## Data: dmerge
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 611.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.89600 -0.65280 -0.09113 0.61185 2.51073
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.4802491 0.69300
## reps1 0.0140203 0.11841 0.58
## shockCond1 0.0090010 0.09487 0.33 0.32
## resp1 0.0006083 0.02466 0.26 0.67 0.55
## Residual 0.1669692 0.40862
## Number of obs: 376, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.80038 0.14450 45.00000 5.539 1.5e-06
## group1 0.56595 0.20656 45.00000 2.740 0.008780
## shockCond1 -0.04184 0.03528 45.00000 -1.186 0.241887
## reps1 0.15129 0.03813 45.00000 3.968 0.000258
## resp1 0.06032 0.02992 45.00000 2.016 0.049754
## shockCond1:reps1 0.04200 0.02949 186.00000 1.424 0.156088
## group1:shockCond1 0.04811 0.05043 45.00000 0.954 0.345255
## group1:reps1 0.11789 0.05451 45.00000 2.163 0.035892
## group1:resp1 -0.04098 0.04277 45.00000 -0.958 0.343029
## group1:shockCond1:reps1 0.01749 0.04216 186.00000 0.415 0.678671
##
## (Intercept) ***
## group1 **
## shockCond1
## reps1 ***
## resp1 *
## shockCond1:reps1
## group1:shockCond1
## group1:reps1 *
## group1:resp1
## group1:shockCond1:reps1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 shckC1 reps1 resp1 shC1:1 gr1:C1 grp1:rp1
## group1 -0.700
## shockCond1 0.179 -0.125
## reps1 0.358 -0.250 0.111
## resp1 0.044 -0.030 0.051 0.071
## shckCnd1:r1 0.000 0.000 0.000 0.000 0.000
## grp1:shckC1 -0.125 0.179 -0.700 -0.077 -0.036 0.000
## group1:rps1 -0.250 0.358 -0.077 -0.700 -0.050 0.000 0.111
## group1:rsp1 -0.030 0.044 -0.036 -0.050 -0.700 0.000 0.051 0.071
## grp1:shC1:1 0.000 0.000 0.000 0.000 0.000 -0.700 0.000 0.000
## grp1:rs1
## group1
## shockCond1
## reps1
## resp1
## shckCnd1:r1
## grp1:shckC1
## group1:rps1
## group1:rsp1
## grp1:shC1:1 0.000contrasts(dmerge$group) = c(1,-1)
# stress means by run type
dmerge %>% filter(group == 'stress') %>%
group_by(subid, shockCond) %>%
summarise(dprime = mean(dprime)) %>%
group_by(shockCond) %>%
summarise(dprime = mean(dprime)) ## # A tibble: 2 x 2
## shockCond dprime
## <fctr> <dbl>
## 1 safe 0.7585424
## 2 threat 0.8422192What about high confidence FA rates?
# Calculate high confidence FA rate
dat_fa = dtest %>%
filter(cond %in% c('foil'),
acc == 'FA') %>%
group_by(subid, group, shockCond, conf) %>%
summarise(count=n()) %>%
ungroup() %>%
complete(nesting(subid, group), shockCond, conf,
fill = list(count = 0)) %>%
filter(conf == 'Hi')
head(dat_fa)## # A tibble: 6 x 5
## subid group shockCond conf count
## <fctr> <fctr> <fctr> <fctr> <dbl>
## 1 ap100 control safe Hi 1
## 2 ap100 control threat Hi 2
## 3 ap101 control safe Hi 1
## 4 ap101 control threat Hi 0
## 5 ap102 control safe Hi 4
## 6 ap102 control threat Hi 4new_counts = dtest %>%
filter(cond %in% c('foil')) %>%
mutate(cond = factor(cond),
acc = factor(acc)) %>%
group_by(subid, group, shockCond) %>%
summarise(count=n()) %>%
ungroup() %>%
complete(nesting(subid, group), shockCond, fill = list(count = 0))
min(new_counts$count)## [1] 28head(new_counts)## # A tibble: 6 x 4
## subid group shockCond count
## <fctr> <fctr> <fctr> <dbl>
## 1 ap100 control safe 37
## 2 ap100 control threat 42
## 3 ap101 control safe 42
## 4 ap101 control threat 42
## 5 ap102 control safe 42
## 6 ap102 control threat 42dat_fa = dat_fa %>%
left_join(new_counts %>% rename(new_count = count)) %>%
mutate(fa_rate = count/new_count)## Joining, by = c("subid", "group", "shockCond")head(dat_fa)## # A tibble: 6 x 7
## subid group shockCond conf count new_count fa_rate
## <fctr> <fctr> <fctr> <fctr> <dbl> <dbl> <dbl>
## 1 ap100 control safe Hi 1 37 0.02702703
## 2 ap100 control threat Hi 2 42 0.04761905
## 3 ap101 control safe Hi 1 42 0.02380952
## 4 ap101 control threat Hi 0 42 0.00000000
## 5 ap102 control safe Hi 4 42 0.09523810
## 6 ap102 control threat Hi 4 42 0.09523810dat_fa %>% group_by(subid, group, shockCond) %>%
summarise(fa_rate = mean(fa_rate)) %>%
group_by(group, shockCond) %>%
summarise(mean_fa = mean(fa_rate), se=std.error(fa_rate), n())## Source: local data frame [4 x 5]
## Groups: group [?]
##
## # A tibble: 4 x 5
## group shockCond mean_fa se `n()`
## <fctr> <fctr> <dbl> <dbl> <int>
## 1 control safe 0.06121650 0.01684408 23
## 2 control threat 0.06366460 0.02103131 23
## 3 stress safe 0.04960317 0.01410897 24
## 4 stress threat 0.05497642 0.01331615 24dat_fa %>% group_by(subid, group) %>%
summarise(fa_rate = mean(fa_rate)) %>%
group_by(group) %>%
summarise(mean_fa = mean(fa_rate), se=std.error(fa_rate), n())## # A tibble: 2 x 4
## group mean_fa se `n()`
## <fctr> <dbl> <dbl> <int>
## 1 control 0.06244055 0.01779309 23
## 2 stress 0.05228980 0.01305370 24hist(dat_fa$fa_rate)
hist(logit(dat_fa$fa_rate))
dat_fa = dat_fa %>% mutate(fa_rate_log = car::logit(fa_rate))## Warning in car::logit(c(0.027027027027027, 0.0476190476190476,
## 0.0238095238095238, : proportions remapped to (0.025, 0.975)contrasts(dat_fa$group) = c(1,-1); contrasts(dat_fa$group)## [,1]
## control 1
## stress -1contrasts(dat_fa$shockCond) = c(1,-1); contrasts(dat_fa$shockCond)## [,1]
## safe 1
## threat -1summary(lmer(fa_rate_log ~ group*shockCond +
(1 | subid), data=dat_fa,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=5e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: fa_rate_log ~ group * shockCond + (1 | subid)
## Data: dat_fa
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 5e+05))
##
## REML criterion at convergence: 219.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.88373 -0.54589 0.03345 0.46221 1.95393
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.4569 0.6760
## Residual 0.2579 0.5079
## Number of obs: 94, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.75856 0.11168 45.00000 -24.701 <2e-16 ***
## group1 0.01933 0.11168 45.00000 0.173 0.863
## shockCond1 -0.02575 0.05240 45.00000 -0.491 0.625
## group1:shockCond1 0.05654 0.05240 45.00000 1.079 0.286
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 shckC1
## group1 0.021
## shockCond1 0.000 0.000
## grp1:shckC1 0.000 0.000 0.021All assoc FA rates:
# Calculate high confidence FA rate
dat_fa = dtest %>%
filter(cond %in% c('foil'),
acc == 'FA') %>%
group_by(subid, group, shockCond, conf) %>%
summarise(count=n()) %>%
ungroup() %>%
complete(nesting(subid, group), shockCond, conf,
fill = list(count = 0))
head(dat_fa)## # A tibble: 6 x 5
## subid group shockCond conf count
## <fctr> <fctr> <fctr> <fctr> <dbl>
## 1 ap100 control safe Hi 1
## 2 ap100 control safe Lo 7
## 3 ap100 control safe N 0
## 4 ap100 control threat Hi 2
## 5 ap100 control threat Lo 6
## 6 ap100 control threat N 0new_counts = dtest %>%
filter(cond %in% c('foil')) %>%
mutate(cond = factor(cond),
acc = factor(acc)) %>%
group_by(subid, group, shockCond) %>%
summarise(count=n()) %>%
ungroup() %>%
complete(nesting(subid, group), shockCond,
fill = list(count = 0))
dat_fa = dat_fa %>%
left_join(new_counts %>% rename(new_count = count)) %>%
mutate(fa_rate = count/new_count)## Joining, by = c("subid", "group", "shockCond")dat_fa %>% group_by(subid, group, conf) %>%
summarise(fa_rate = mean(fa_rate)) %>%
group_by(group, conf) %>%
summarise(mean_fa = mean(fa_rate), se=std.error(fa_rate), n())## Source: local data frame [6 x 5]
## Groups: group [?]
##
## # A tibble: 6 x 5
## group conf mean_fa se `n()`
## <fctr> <fctr> <dbl> <dbl> <int>
## 1 control Hi 0.06244055 0.01779309 23
## 2 control Lo 0.26653517 0.04050742 23
## 3 control N 0.00000000 0.00000000 23
## 4 stress Hi 0.05228980 0.01305370 24
## 5 stress Lo 0.30960598 0.04339815 24
## 6 stress N 0.00000000 0.00000000 24dat_fa %>% group_by(subid, group, shockCond, conf) %>%
summarise(fa_rate = mean(fa_rate)) %>%
group_by(group, conf, shockCond) %>%
summarise(mean_fa = mean(fa_rate), se=std.error(fa_rate), n())## Source: local data frame [12 x 6]
## Groups: group, conf [?]
##
## # A tibble: 12 x 6
## group conf shockCond mean_fa se `n()`
## <fctr> <fctr> <fctr> <dbl> <dbl> <int>
## 1 control Hi safe 0.06121650 0.01684408 23
## 2 control Hi threat 0.06366460 0.02103131 23
## 3 control Lo safe 0.26909518 0.04101087 23
## 4 control Lo threat 0.26397516 0.04223021 23
## 5 control N safe 0.00000000 0.00000000 23
## 6 control N threat 0.00000000 0.00000000 23
## 7 stress Hi safe 0.04960317 0.01410897 24
## 8 stress Hi threat 0.05497642 0.01331615 24
## 9 stress Lo safe 0.31646825 0.04805868 24
## 10 stress Lo threat 0.30274370 0.04141771 24
## 11 stress N safe 0.00000000 0.00000000 24
## 12 stress N threat 0.00000000 0.00000000 24All assoc H rates:
# Calculate high confidence H rates
dat_fa = dtest %>%
filter(cond %in% c('indoor', 'outdoor'),
acc == 'H') %>%
mutate(cond = factor(cond),
acc = factor(acc)) %>%
group_by(subid, group, shockCond, conf, cond) %>%
summarise(count=n()) %>%
ungroup() %>%
complete(nesting(subid, group), shockCond, conf, cond,
fill = list(count = 0))
head(dat_fa)## # A tibble: 6 x 6
## subid group shockCond conf cond count
## <fctr> <fctr> <fctr> <fctr> <fctr> <dbl>
## 1 ap100 control safe Hi indoor 22
## 2 ap100 control safe Hi outdoor 24
## 3 ap100 control safe Lo indoor 3
## 4 ap100 control safe Lo outdoor 0
## 5 ap100 control safe N indoor 0
## 6 ap100 control safe N outdoor 0old_counts = dtest %>%
filter(cond %in% c('indoor', 'outdoor')) %>%
mutate(cond = factor(cond),
acc = factor(acc)) %>%
group_by(subid, group, shockCond, cond) %>%
summarise(count=n()) %>%
ungroup() %>%
complete(nesting(subid, group), shockCond, cond,
fill = list(count = 0))
dat_fa = dat_fa %>%
left_join(old_counts %>% rename(old_count = count)) %>%
mutate(hit_rate = count/old_count)## Joining, by = c("subid", "group", "shockCond", "cond")dat_fa %>% group_by(subid, group, conf) %>%
summarise(hit_rate = mean(hit_rate)) %>%
group_by(group, conf) %>%
summarise(mean(hit_rate), std.error(hit_rate), n())## Source: local data frame [6 x 5]
## Groups: group [?]
##
## # A tibble: 6 x 5
## group conf `mean(hit_rate)` `std.error(hit_rate)` `n()`
## <fctr> <fctr> <dbl> <dbl> <int>
## 1 control Hi 0.4886196 0.04835648 23
## 2 control Lo 0.1638104 0.02223038 23
## 3 control N 0.0000000 0.00000000 23
## 4 stress Hi 0.3016106 0.03293669 24
## 5 stress Lo 0.2152586 0.02335922 24
## 6 stress N 0.0000000 0.00000000 24dat_fa %>% filter(group == 'stress') %>%
group_by(subid, group, conf, shockCond) %>%
summarise(hit_rate = mean(hit_rate)) %>%
group_by(group, conf, shockCond) %>%
summarise(mean(hit_rate), std.error(hit_rate), n())## Source: local data frame [6 x 6]
## Groups: group, conf [?]
##
## # A tibble: 6 x 6
## group conf shockCond `mean(hit_rate)` `std.error(hit_rate)` `n()`
## <fctr> <fctr> <fctr> <dbl> <dbl> <int>
## 1 stress Hi safe 0.2986777 0.03246858 24
## 2 stress Hi threat 0.3045435 0.03488316 24
## 3 stress Lo safe 0.2169879 0.02301797 24
## 4 stress Lo threat 0.2135294 0.02466803 24
## 5 stress N safe 0.0000000 0.00000000 24
## 6 stress N threat 0.0000000 0.00000000 24All assoc error (miss) rates:
# Calculate high confidence H rates
dat_fa = dtest %>%
filter(cond %in% c('indoor', 'outdoor'),
acc == 'SM') %>%
mutate(cond = factor(cond),
acc = factor(acc)) %>%
group_by(subid, group, shockCond, conf, cond) %>%
summarise(count=n()) %>%
ungroup() %>%
complete(nesting(subid, group), shockCond, conf, cond,
fill = list(count = 0))
head(dat_fa)## # A tibble: 6 x 6
## subid group shockCond conf cond count
## <fctr> <fctr> <fctr> <fctr> <fctr> <dbl>
## 1 ap100 control safe Hi indoor 3
## 2 ap100 control safe Hi outdoor 2
## 3 ap100 control safe Lo indoor 2
## 4 ap100 control safe Lo outdoor 4
## 5 ap100 control safe N indoor 0
## 6 ap100 control safe N outdoor 0old_counts = dtest %>%
filter(cond %in% c('indoor', 'outdoor')) %>%
mutate(cond = factor(cond),
acc = factor(acc)) %>%
group_by(subid, group, shockCond, cond) %>%
summarise(count=n()) %>%
ungroup() %>%
complete(nesting(subid, group), shockCond, cond,
fill = list(count = 0))
dat_fa = dat_fa %>%
left_join(old_counts %>% rename(old_count = count)) %>%
mutate(hit_rate = count/old_count)## Joining, by = c("subid", "group", "shockCond", "cond")dat_fa %>% group_by(subid, group, conf) %>%
summarise(hit_rate = mean(hit_rate)) %>%
group_by(group, conf) %>%
summarise(mean(hit_rate), std.error(hit_rate), n())## Source: local data frame [6 x 5]
## Groups: group [?]
##
## # A tibble: 6 x 5
## group conf `mean(hit_rate)` `std.error(hit_rate)` `n()`
## <fctr> <fctr> <dbl> <dbl> <int>
## 1 control Hi 0.09469007 0.01150091 23
## 2 control Lo 0.13231420 0.01996173 23
## 3 control N 0.00000000 0.00000000 23
## 4 stress Hi 0.09025433 0.01282108 24
## 5 stress Lo 0.20250565 0.02215405 24
## 6 stress N 0.00000000 0.00000000 24dat_fa %>% filter(group == 'stress') %>%
group_by(subid, group, conf, shockCond) %>%
summarise(hit_rate = mean(hit_rate)) %>%
group_by(group, conf, shockCond) %>%
summarise(mean(hit_rate), std.error(hit_rate), n())## Source: local data frame [6 x 6]
## Groups: group, conf [?]
##
## # A tibble: 6 x 6
## group conf shockCond `mean(hit_rate)` `std.error(hit_rate)` `n()`
## <fctr> <fctr> <fctr> <dbl> <dbl> <int>
## 1 stress Hi safe 0.10026132 0.01646251 24
## 2 stress Hi threat 0.08024734 0.01083886 24
## 3 stress Lo safe 0.19875750 0.02265572 24
## 4 stress Lo threat 0.20625380 0.02357937 24
## 5 stress N safe 0.00000000 0.00000000 24
## 6 stress N threat 0.00000000 0.00000000 24Does cortisol affect assoc dprime?
### Does cortisol affect assoc dprime?
d_cort = read.csv('/Volumes/group/awagner/sgagnon/AP/data/cortisol/cort_percentchange_testbaseline.csv')
dprime_cort = dmerge %>% left_join(d_cort)## Joining, by = c("subid", "group")str(dprime_cort)## Classes 'tbl_df', 'tbl' and 'data.frame': 376 obs. of 12 variables:
## $ subid : Factor w/ 47 levels "ap100","ap101",..: 1 1 1 1 1 1 1 1 2 2 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] 1 -1
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr "control" "stress"
## .. .. ..$ : NULL
## $ shockCond : Factor w/ 2 levels "safe","threat": 1 1 1 1 2 2 2 2 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] 1 -1
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr "safe" "threat"
## .. .. ..$ : NULL
## $ reps : Factor w/ 2 levels "2","4": 1 1 2 2 1 1 2 2 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -1 1
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr "2" "4"
## .. .. ..$ : NULL
## $ resp : Factor w/ 2 levels "indoor","outdoor": 1 2 1 2 1 2 1 2 1 2 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -1 1
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr "indoor" "outdoor"
## .. .. ..$ : NULL
## $ H : num 0.5 0.556 0.667 0.7 0.476 ...
## $ SM : num 0.0556 0.025 0.05 0.1667 0.0476 ...
## $ dprime : num 1.59 2.1 2.08 1.49 1.61 ...
## $ c : num 0.797 0.91 0.607 0.222 0.864 ...
## $ X : int 0 0 0 0 0 0 0 0 1 1 ...
## $ assay : Factor w/ 2 levels "IBL","Salimetrics": 1 1 1 1 1 1 1 1 1 1 ...
## $ log.ug.dL.: num -7.36 -7.36 -7.36 -7.36 -7.36 ...contrasts(dprime_cort$shockCond) = c(1,-1)
contrasts(dprime_cort$reps) = c(-1,1)
contrasts(dprime_cort$assay) = c(-1,1)
contrasts(dprime_cort$resp) = c(-1,1); contrasts(dprime_cort$resp)## [,1]
## indoor -1
## outdoor 1# include group interactions by resp, shockcond, reps
fit = lmer(dprime ~ scale(log.ug.dL.) * (shockCond * reps + resp) + assay+
(1 + reps + shockCond + resp| subid), data=dprime_cort,
control=lmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5)))
fit2 = lmer(dprime ~ poly(log.ug.dL.,2) * (shockCond * reps + resp) + assay+
(1 + reps + shockCond + resp| subid), data=dprime_cort,
control=lmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5)))
anova(fit, fit2)## refitting model(s) with ML (instead of REML)## Data: dprime_cort
## Models:
## object: dprime ~ scale(log.ug.dL.) * (shockCond * reps + resp) + assay +
## object: (1 + reps + shockCond + resp | subid)
## ..1: dprime ~ poly(log.ug.dL., 2) * (shockCond * reps + resp) + assay +
## ..1: (1 + reps + shockCond + resp | subid)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## object 22 616.46 702.91 -286.23 572.46
## ..1 27 621.92 728.01 -283.96 567.92 4.5454 5 0.4738summary(fit)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: dprime ~ scale(log.ug.dL.) * (shockCond * reps + resp) + assay +
## (1 + reps + shockCond + resp | subid)
## Data: dprime_cort
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 626.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.92749 -0.64163 -0.07639 0.61270 2.58371
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.5684942 0.75399
## reps1 0.0160436 0.12666 0.71
## shockCond1 0.0095079 0.09751 0.39 0.44
## resp1 0.0008988 0.02998 -0.12 0.31 0.23
## Residual 0.1669770 0.40863
## Number of obs: 376, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.079974 0.114447 45.210000
## scale(log.ug.dL.) 0.073057 0.112158 44.390000
## shockCond1 -0.018297 0.025424 45.000000
## reps1 0.208980 0.028026 45.000000
## resp1 0.040267 0.021522 45.000000
## assay1 0.011269 0.100963 44.000000
## shockCond1:reps1 0.050556 0.021073 186.000000
## scale(log.ug.dL.):shockCond1 0.009607 0.025458 45.000000
## scale(log.ug.dL.):reps1 -0.039233 0.028063 45.000000
## scale(log.ug.dL.):resp1 0.011876 0.021551 45.000000
## scale(log.ug.dL.):shockCond1:reps1 -0.008532 0.021101 186.000000
## t value Pr(>|t|)
## (Intercept) 9.436 2.96e-12 ***
## scale(log.ug.dL.) 0.651 0.5182
## shockCond1 -0.720 0.4754
## reps1 7.457 2.15e-09 ***
## resp1 1.871 0.0679 .
## assay1 0.112 0.9116
## shockCond1:reps1 2.399 0.0174 *
## scale(log.ug.dL.):shockCond1 0.377 0.7077
## scale(log.ug.dL.):reps1 -1.398 0.1690
## scale(log.ug.dL.):resp1 0.551 0.5843
## scale(log.ug.dL.):shockCond1:reps1 -0.404 0.6864
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(..L.) shckC1 reps1 resp1 assay1 shC1:1
## scl(lg..L.) -0.005
## shockCond1 0.208 0.000
## reps1 0.449 0.000 0.163
## resp1 -0.023 0.000 0.026 0.042
## assay1 0.206 -0.022 0.000 0.000 0.000
## shckCnd1:r1 0.000 0.000 0.000 0.000 0.000 0.000
## sc(..L.):C1 0.000 0.212 0.000 0.000 0.000 0.000 0.000
## scl(lg.g.dL.):rp1 0.000 0.459 0.000 0.000 0.000 0.000 0.000
## scl(lg.g.dL.):rs1 0.000 -0.024 0.000 0.000 0.000 0.000 0.000
## s(..L.):C1: 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## sc(..L.):C1 scl(lg.g.dL.):rp1 scl(lg.g.dL.):rs1
## scl(lg..L.)
## shockCond1
## reps1
## resp1
## assay1
## shckCnd1:r1
## sc(..L.):C1
## scl(lg.g.dL.):rp1 0.163
## scl(lg.g.dL.):rs1 0.026 0.042
## s(..L.):C1: 0.000 0.000 0.0001c) Plot item and assoc dprimes
# merge these 2 measures:
item_d = item_d %>%
dplyr::select(subid, group, reps, shockCond, dprime) %>%
group_by(subid, group, reps, shockCond) %>%
summarise(item_d = mean(dprime))
assoc_d = assoc_d %>%
dplyr::select(subid, group, reps, shockCond, dprime) %>%
group_by(subid, group, reps, shockCond) %>%
summarise(assoc_d = mean(dprime))
combined_d = item_d %>% left_join(assoc_d) %>%
ungroup() %>%
gather(key = 'cond', value = 'dprime', item_d:assoc_d) %>%
mutate(cond = factor(cond), reps=factor(reps))## Joining, by = c("subid", "group", "reps", "shockCond")combined_d## # A tibble: 376 x 6
## subid group reps shockCond cond dprime
## <fctr> <fctr> <fctr> <fctr> <fctr> <dbl>
## 1 ap100 control 2 safe item_d 1.5015336
## 2 ap100 control 2 threat item_d 1.5885859
## 3 ap100 control 4 safe item_d 1.6844710
## 4 ap100 control 4 threat item_d 1.8435644
## 5 ap101 control 2 safe item_d 2.4114797
## 6 ap101 control 2 threat item_d 2.6931954
## 7 ap101 control 4 safe item_d 3.4459862
## 8 ap101 control 4 threat item_d 2.8568952
## 9 ap102 control 2 safe item_d 0.8767526
## 10 ap102 control 2 threat item_d 0.8508243
## # ... with 366 more rowsdavg = combined_d %>%
group_by(subid, group, cond, reps) %>%
summarise(dprime = mean(dprime)) %>%
group_by(group, cond, reps) %>%
summarise(mean = mean(dprime),
se = std.error(dprime),
sterr = sd(dprime)/sqrt(n()),
ci = error <- qt(1-(.05/2),df=n()-1)*sd(dprime)/sqrt(n()),
sd = sd(dprime),
n()) %>%
ungroup() %>%
mutate(cond = as.character(cond)) %>%
mutate(cond=replace(cond, cond=='assoc_d', 'Associative'),
cond=replace(cond, cond=='item_d', 'Item')) %>%
mutate(cond = factor(cond, levels=c('Item', 'Associative'))) %>%
mutate(reps = as.character(reps)) %>%
mutate(reps=replace(reps, reps=='2', 'weak'),
reps=replace(reps, reps=='4', 'strong')) %>%
mutate(reps = factor(reps, levels=c('weak', 'strong')))
davg## # A tibble: 8 x 9
## group cond reps mean se sterr ci
## <fctr> <fctr> <fctr> <dbl> <dbl> <dbl> <dbl>
## 1 control Associative weak 1.0971524 0.1564134 0.1564134 0.3243814
## 2 control Associative strong 1.6355152 0.1853442 0.1853442 0.3843803
## 3 control Item weak 1.7018254 0.1817859 0.1817859 0.3770009
## 4 control Item strong 2.0871727 0.1974001 0.1974001 0.4093827
## 5 stress Associative weak 0.6490942 0.1164531 0.1164531 0.2409017
## 6 stress Associative strong 0.9516674 0.1411232 0.1411232 0.2919356
## 7 stress Item weak 1.2568043 0.1108637 0.1108637 0.2293390
## 8 stress Item strong 1.7006655 0.1382036 0.1382036 0.2858959
## # ... with 2 more variables: sd <dbl>, `n()` <int>p = ggplot(davg,
aes(x=reps, y=mean, group=group, fill=group)) +
facet_grid(.~cond, scales = "free_x", space = "free_x") +
geom_bar(position=position_dodge(), stat="identity") +
geom_errorbar(width=0, aes(ymin=mean-se,
ymax=mean+se), size=1.5, position=position_dodge(.9)) +
scale_fill_manual(values=c('dodgerblue', 'orange')) +
ylab(expression(paste('Discriminability (',italic("d'"),')')))+
xlab('Encoding strength') +
theme(legend.title=element_blank(), axis.title.y=element_text(face="italic"))
ggsave('~/Experiments/AP/figs/AP_behav_dprimes_bystrength.jpeg', dpi=150, width=8, height=4)
p
dcomb = combined_d %>%
group_by(subid, group, cond, reps) %>%
summarise(dprime = mean(dprime)) %>%
ungroup() %>%
mutate(cond = as.character(cond)) %>%
mutate(cond=replace(cond, cond=='assoc_d', 'Associative'),
cond=replace(cond, cond=='item_d', 'Item')) %>%
mutate(cond = factor(cond, levels=c('Item', 'Associative'))) %>%
mutate(reps = as.character(reps)) %>%
mutate(reps=replace(reps, reps=='2', 'weak'),
reps=replace(reps, reps=='4', 'strong')) %>%
mutate(reps = factor(reps, levels=c('weak', 'strong'))) %>%
ungroup()
dcomb## # A tibble: 188 x 5
## subid group cond reps dprime
## <fctr> <fctr> <fctr> <fctr> <dbl>
## 1 ap100 control Associative weak 1.6377555
## 2 ap100 control Associative strong 1.5468521
## 3 ap100 control Item weak 1.5450597
## 4 ap100 control Item strong 1.7640177
## 5 ap101 control Associative weak 1.3064383
## 6 ap101 control Associative strong 2.8341356
## 7 ap101 control Item weak 2.5523376
## 8 ap101 control Item strong 3.1514407
## 9 ap102 control Associative weak 0.5144762
## 10 ap102 control Associative strong 1.0402563
## # ... with 178 more rowsp = ggplot(dcomb,
aes(x=reps, y=dprime, color=group)) +
facet_grid(.~cond) +
geom_boxplot(position = position_dodge(width = .85),
outlier.shape = NA) +
scale_x_discrete(name = "Encoding strength") +
geom_point(pch = 21, position = position_jitterdodge())+
scale_color_manual(values=c('dodgerblue', 'orange')) +
ylab(expression(paste('Discriminability (',italic("d'"),')')))+
theme(legend.title=element_blank(), axis.title.y=element_text(face="italic"))
ggsave('~/Experiments/AP/figs/AP_behav_dprimes_bystrength_boxplot.jpeg', dpi=150, width=8, height=4)
p
p = ggplot(dcomb,
aes(x=reps, y=dprime, color=group)) +
facet_grid(.~cond) +
stat_summary(fun.y = "mean", size = 9, shape=18, alpha=.7,
geom = "point", position=position_dodge(.9)) +
scale_x_discrete(name = "Encoding strength") +
geom_point(pch = 21, position = position_jitterdodge(), alpha=1, size=3)+
scale_color_manual(values=c('dodgerblue', 'orange')) +
ylab(expression(paste('Discriminability (',italic("d'"),')')))+
theme(legend.title=element_blank(), axis.title.y=element_text(face="italic"))
p
ggsave('~/Experiments/AP/figs/AP_behav_dprimes_bystrength_stripplot.jpeg', dpi=150, width=8, height=4)
p
# point plot with points
p = ggplot(dcomb,
aes(x=reps, y=dprime, color=group)) +
facet_grid(.~cond) +
# stat_summary(fun.y = "mean", size = 9, shape=18, alpha=.7,
# geom = "point", position=position_dodge(.9)) +
geom_point(pch = 21, position = position_jitterdodge(),
alpha=1, size=3)+
scale_x_discrete(name = "Encoding strength") +
geom_point(data = davg, aes(y=mean), position=position_dodge(.9),
alpha=.7, size = 3, shape=18) +
geom_errorbar(data = davg, width=0,
aes(y=mean, ymin=mean-se, ymax=mean+se),
size=2, position=position_dodge(.9), alpha=.7) +
scale_color_manual(values=c('dodgerblue', 'orange')) +
ylab(expression(paste('Discriminability (',italic("d'"),')')))+
theme(legend.title=element_blank(), axis.title.y=element_text(face="italic"))## Warning: Ignoring unknown aesthetics: yp
p = ggplot(dcomb,
aes(x=reps, y=dprime, color=group)) +
facet_grid(.~cond) +
geom_point(pch = 21, position = position_jitterdodge(),
alpha=.5, size=2)+
scale_x_discrete(name = "Encoding strength") +
geom_point(data = davg, aes(y=mean), position=position_dodge(.9),
alpha=.8, size = 2.5, shape=19) +
geom_errorbar(data = davg, width=0,
aes(y=mean, ymin=mean-se, ymax=mean+se),
size=1.5, position=position_dodge(.9), alpha=.8) +
scale_color_manual(values=c('dodgerblue', 'orange')) +
ylab(expression(paste('Discriminability (',italic("d'"),')')))+
theme(legend.title=element_blank(), axis.title.y=element_text(face="italic"))## Warning: Ignoring unknown aesthetics: yggsave('~/Experiments/AP/figs/AP_behav_dprimes_bystrength_stripplot_se.jpeg', dpi=600, width=8, height=4)
p
p = ggplot(dcomb,
aes(x=reps, y=dprime, color=group)) +
facet_grid(.~cond) +
geom_point(pch = 19, position = position_jitterdodge(),
alpha=.2, size=1.5)+
scale_x_discrete(name = "Encoding strength") +
geom_point(data = davg, aes(y=mean), position=position_dodge(.9),
alpha=1, size = 2.5, shape=19) +
geom_errorbar(data = davg, width=0,
aes(y=mean, ymin=mean-se, ymax=mean+se),
size=1.5, position=position_dodge(.9), alpha=1) +
scale_color_manual(values=c('dodgerblue', 'orange')) +
ylab(expression(paste('Discriminability (',italic("d'"),')')))+
theme_classic(base_size = 14)+
theme(legend.title=element_blank(),
strip.text = element_text(size=14),
strip.background = element_rect(colour="white", fill="white"),
axis.title.y=element_text(face="italic"))## Warning: Ignoring unknown aesthetics: yggsave('~/Experiments/AP/figs/AP_behav_dprimes_bystrength_stripplot_se_v2.jpeg', dpi=600, width=6.69, height=3)
ggsave('~/Dropbox/Stanford/Papers/AP/Figures/AP_figure2.tiff', dpi=600, width=6.69, height=3)
p
ann_text <- data.frame(reps = c(.75, 1.25), dprime = c(4, 3.5),
group=c('control', 'stress'),
lab=c("control", "stress"),
cond = factor(c('Item'), levels = c("Item", "Associative")))
# 95% CI
p = ggplot(dcomb,
aes(x=reps, y=dprime, color=group)) +
facet_grid(.~cond) +
geom_point(pch = 19, position = position_jitterdodge(),
alpha=.2, size=1.5)+
scale_x_discrete(name = "Encoding strength") +
geom_errorbar(data = davg, width=0,
aes(y=mean, ymin=mean-ci, ymax=mean+ci),
size=1.5, position=position_dodge(.9), alpha=1) +
geom_point(data = davg, aes(y=mean), position=position_dodge(.9),
alpha=1, size = 4, shape=1) +
scale_color_manual(values=c('dodgerblue', 'orange')) +
ylab(expression(paste('Discriminability (',italic("d'"),')')))+
geom_text(data = ann_text,aes(label =lab)) +
theme_classic(base_size = 14)+
theme(legend.title=element_blank(),
strip.background = element_rect(colour="white", fill="white"),
legend.position="none",
axis.title.y=element_text(face="italic"))## Warning: Ignoring unknown aesthetics: yggsave('~/Experiments/AP/figs/AP_behav_dprimes_bystrength_stripplot_95ci_v2.jpeg', dpi=600, width=6.69, height=3)
ggsave('~/Dropbox/Stanford/Papers/AP/Figures/AP_figure2_95ci.jpg', dpi=600, width=6.69, height=3)
p
Just assoc plot
# p = ggplot(davg %>% filter(cond == 'associative'),
# aes(x=reps, y=mean, group=group, fill=group)) +
# geom_bar(position=position_dodge(), stat="identity") +
# geom_errorbar(width=0, aes(ymin=mean-se,
# ymax=mean+se), size=1.5, position=position_dodge(.9)) +
# scale_fill_manual(values=c('dodgerblue', 'orange')) +
# ylab(expression(paste('Associative ',italic("d'"))))+
# xlab('Encoding strength') +
# ylim(0, 1.9) +
# theme(legend.title=element_blank(), axis.title.y=element_text(face="italic"))
#
# ggsave('~/Dropbox/Stanford/Papers/Dissertation/Figures_defense/AP_behav_assocdprime_bystrength.jpeg',
# dpi=300, width=6, height=4)
# pdavg = combined_d %>%
filter(group == 'stress') %>%
group_by(subid, group, cond, reps, shockCond) %>%
summarise(dprime = mean(dprime)) %>%
group_by(group, cond, reps, shockCond) %>%
summarise(mean = mean(dprime),
se = std.error(dprime),
sterr = sd(dprime)/sqrt(n()),
n()) %>%
ungroup() %>%
mutate(cond = as.character(cond)) %>%
mutate(cond=replace(cond, cond=='assoc_d', 'associative'),
cond=replace(cond, cond=='item_d', 'item')) %>%
mutate(cond = factor(cond, levels=c('item', 'associative'))) %>%
mutate(reps = as.character(reps)) %>%
mutate(reps=replace(reps, reps=='2', 'weak'),
reps=replace(reps, reps=='4', 'strong')) %>%
mutate(reps = factor(reps, levels=c('weak', 'strong')))
davg## # A tibble: 8 x 8
## group cond reps shockCond mean se sterr `n()`
## <fctr> <fctr> <fctr> <fctr> <dbl> <dbl> <dbl> <int>
## 1 stress associative weak safe 0.5652593 0.1241485 0.1241485 24
## 2 stress associative weak threat 0.7329291 0.1265974 0.1265974 24
## 3 stress associative strong safe 0.9518255 0.1692885 0.1692885 24
## 4 stress associative strong threat 0.9515093 0.1345798 0.1345798 24
## 5 stress item weak safe 1.2597242 0.1353836 0.1353836 24
## 6 stress item weak threat 1.2538845 0.1119375 0.1119375 24
## 7 stress item strong safe 1.7282156 0.1638694 0.1638694 24
## 8 stress item strong threat 1.6731154 0.1295086 0.1295086 24p=ggplot(davg,
aes(x=reps, y=mean, group=shockCond, fill=shockCond)) +
facet_grid(.~cond, scales = "free_x", space = "free_x") +
geom_bar(position=position_dodge(), stat="identity") +
geom_errorbar(width=0, aes(ymin=mean-se,
ymax=mean+se), size=1.5, position=position_dodge(.9)) +
scale_fill_manual(values=c('mediumpurple', 'darkorange')) +
ylab(expression(paste('Discriminability (',italic("d'"),')')))+
xlab('Encoding strength') +
theme(legend.title=element_blank(), axis.title.y=element_text(face="italic"))
ggsave('~/Experiments/AP/figs/AP_behav_dprimes_stress_byshockCond.jpeg', dpi=150, width=8, height=4)
p
Stress: within-participant standard error/95% CI (if all subjs)
davg = combined_d %>%
filter(group == 'stress') %>%
group_by(subid, group, cond, reps, shockCond) %>%
summarise(dprime = mean(dprime))
davg## Source: local data frame [192 x 6]
## Groups: subid, group, cond, reps [?]
##
## # A tibble: 192 x 6
## subid group cond reps shockCond dprime
## <fctr> <fctr> <fctr> <fctr> <fctr> <dbl>
## 1 ap150 stress assoc_d 2 safe 1.6753826
## 2 ap150 stress assoc_d 2 threat 0.9467194
## 3 ap150 stress assoc_d 4 safe 1.5926593
## 4 ap150 stress assoc_d 4 threat 1.2552104
## 5 ap150 stress item_d 2 safe 1.3091717
## 6 ap150 stress item_d 2 threat 1.9073581
## 7 ap150 stress item_d 4 safe 1.6567948
## 8 ap150 stress item_d 4 threat 1.3816142
## 9 ap151 stress assoc_d 2 safe -0.1561806
## 10 ap151 stress assoc_d 2 threat 0.0000000
## # ... with 182 more rowsdfwc = summarySEwithin(davg, measurevar="dprime", withinvars=c("cond", "reps", "shockCond"),
idvar="subid", na.rm=TRUE, conf.interval=.95) %>%
ungroup() %>%
mutate(cond = as.character(cond)) %>%
mutate(cond=replace(cond, cond=='assoc_d', 'Associative'),
cond=replace(cond, cond=='item_d', 'Item')) %>%
mutate(cond = factor(cond, levels=c('Item', 'Associative'))) %>%
mutate(reps = as.character(reps)) %>%
mutate(reps=replace(reps, reps=='2', 'weak'),
reps=replace(reps, reps=='4', 'strong')) %>%
mutate(reps = factor(reps, levels=c('weak', 'strong')))
dfwc## cond reps shockCond N dprime dprime_norm sd
## 1 Associative weak safe 24 0.5652593 0.5652593 0.3927540
## 2 Associative weak threat 24 0.7329291 0.7329291 0.3658351
## 3 Associative strong safe 24 0.9518255 0.9518255 0.5436313
## 4 Associative strong threat 24 0.9515093 0.9515093 0.3729508
## 5 Item weak safe 24 1.2597242 1.2597242 0.4441552
## 6 Item weak threat 24 1.2538845 1.2538845 0.3616066
## 7 Item strong safe 24 1.7282156 1.7282156 0.5007407
## 8 Item strong threat 24 1.6731154 1.6731154 0.4045086
## se ci
## 1 0.08017058 0.1658455
## 2 0.07467578 0.1544786
## 3 0.11096828 0.2295554
## 4 0.07612826 0.1574833
## 5 0.09066279 0.1875503
## 6 0.07381263 0.1526931
## 7 0.10221327 0.2114443
## 8 0.08256997 0.1708090p=ggplot(dfwc,
aes(x=reps, y=dprime, group=shockCond, fill=shockCond)) +
facet_grid(.~cond, scales = "free_x", space = "free_x") +
geom_bar(position=position_dodge(), stat="identity") +
geom_errorbar(width=0, aes(ymin=dprime-se,
ymax=dprime+se), size=1.5, position=position_dodge(.9)) +
scale_fill_manual(values=c('mediumpurple', 'darkorange')) +
ylab(expression(paste('Discriminability (',italic("d'"),')')))+
xlab('Encoding strength') +
theme(legend.title=element_blank(), axis.title.y=element_text(face="italic"))
ggsave('~/Experiments/AP/figs/AP_behav_dprimes_stress_byshockCond_withinsubjerrorbars.jpeg', dpi=300, width=8, height=4)
p
### include points for each subj
dcomb = combined_d %>%
filter(group == 'stress') %>%
group_by(subid, group, shockCond, cond, reps) %>%
summarise(dprime = mean(dprime)) %>%
ungroup() %>%
mutate(cond = as.character(cond)) %>%
mutate(cond=replace(cond, cond=='assoc_d', 'Associative'),
cond=replace(cond, cond=='item_d', 'Item')) %>%
mutate(cond = factor(cond, levels=c('Item', 'Associative'))) %>%
mutate(reps = as.character(reps)) %>%
mutate(reps=replace(reps, reps=='2', 'weak'),
reps=replace(reps, reps=='4', 'strong')) %>%
mutate(reps = factor(reps, levels=c('weak', 'strong'))) %>%
ungroup()
dcomb## # A tibble: 192 x 6
## subid group shockCond cond reps dprime
## <fctr> <fctr> <fctr> <fctr> <fctr> <dbl>
## 1 ap150 stress safe Associative weak 1.6753826
## 2 ap150 stress safe Associative strong 1.5926593
## 3 ap150 stress safe Item weak 1.3091717
## 4 ap150 stress safe Item strong 1.6567948
## 5 ap150 stress threat Associative weak 0.9467194
## 6 ap150 stress threat Associative strong 1.2552104
## 7 ap150 stress threat Item weak 1.9073581
## 8 ap150 stress threat Item strong 1.3816142
## 9 ap151 stress safe Associative weak -0.1561806
## 10 ap151 stress safe Associative strong 0.1796097
## # ... with 182 more rowsp = ggplot(dcomb,
aes(x=reps, y=dprime, color=shockCond)) +
facet_grid(.~cond) +
geom_point(pch = 19, position = position_jitterdodge(),
alpha=.2, size=1.5)+
scale_x_discrete(name = "Encoding strength") +
geom_point(data = dfwc, aes(y=dprime), position=position_dodge(.9),
alpha=1, size = 2.5, shape=19) +
geom_errorbar(data = dfwc, width=0,
aes(y=dprime, ymin=dprime-ci, ymax=dprime+ci),
size=1.5, position=position_dodge(.9), alpha=1) +
scale_color_manual(values=c('#0072b2', '#d55e00')) +
ylab(expression(paste('Discriminability (',italic("d'"),')')))+
theme_classic(base_size = 14)+
theme(legend.title=element_blank(),
strip.text = element_text(size=14),
strip.background = element_rect(colour="white", fill="white"),
axis.title.y=element_text(face="italic"))## Warning: Ignoring unknown aesthetics: yp
ggsave('~/Dropbox/Stanford/Papers/AP/Figures/AP_supp_figure2.tiff', dpi=600, width=6.69, height=3)
ggsave('~/Dropbox/Stanford/Papers/AP/Figures/AP_supp_figure2_dpi150.tiff', dpi=150, width=6.69, height=3)1d) RT
HC hit, LC hit, CR
dt = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/mvpa/notebooks/ap_behav.csv')
with(dt, table(shockTrial, mem_conditions, group))## , , group = control-fmri
##
## mem_conditions
## shockTrial CR FA itemhit_lo M nuisance sourcehit sourcemiss_hi
## 0 1254 622 1126 426 75 1833 355
## 1 21 13 16 3 2 31 6
##
## , , group = stress-fmri
##
## mem_conditions
## shockTrial CR FA itemhit_lo M nuisance sourcehit sourcemiss_hi
## 0 1204 700 1606 682 207 1164 348
## 1 0 0 0 0 95 0 0# Get mean of median RT by condition, only include subjs w/more than 5 trials/cond
drt = dt %>%
filter(mem_conditions %in% c("sourcehit", 'CR', 'itemhit_lo')) %>%
group_by(group, subid, mem_conditions) %>%
summarise(rt=median(respRT), rt_sd = sd(respRT), n=n()) %>%
filter(n > 5) %>%
group_by(group, mem_conditions) %>%
summarise(mean=mean(rt), se=std.error(rt), n()) %>%
ungroup() %>%
mutate(group = as.character(group),
group = replace(group, group == 'control-fmri', 'control'),
group = replace(group, group == 'stress-fmri', 'stress'),
mem_conditions = as.character(mem_conditions),
mem_conditions = replace(mem_conditions, mem_conditions=='sourcehit', 'HC\nassoc hit'),
mem_conditions = replace(mem_conditions, mem_conditions=='itemhit_lo', 'LC\nitem hit'),
mem_conditions = factor(mem_conditions, levels=c('HC\nassoc hit',
'LC\nitem hit',
'CR')))
drt## # A tibble: 6 x 5
## group mem_conditions mean se `n()`
## <chr> <fctr> <dbl> <dbl> <int>
## 1 control CR 2.011815 0.07130370 23
## 2 control LC
## item hit 2.568783 0.12211904 21
## 3 control HC
## assoc hit 2.221578 0.07417229 23
## 4 stress CR 1.832327 0.07363891 24
## 5 stress LC
## item hit 2.393730 0.08213239 22
## 6 stress HC
## assoc hit 2.271007 0.07712911 22p1=ggplot(drt,
aes(x=mem_conditions, y=mean, group=group, color=group)) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=mean-se,
ymax=mean+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
scale_color_manual(values=c('dodgerblue', 'orange')) +
ylab('Median RT (s)')+
xlab('') +
theme(legend.title = element_blank(),
legend.position = c(0.9, 0.8))
p1
ggsave('~/Experiments/AP/figs/AP_behav_rt_groupXcond.jpeg', dpi=150, width=6, height=4)
p1
HC assoc hit RT - all trials
drt = dt %>%
filter(mem_conditions %in% c("sourcehit")) %>% # need at least 6 trials/bin
group_by(group, subid, mem_conditions) %>%
summarise(n=n()) %>%
ungroup() %>%
mutate(mem_conditions = factor(mem_conditions)) %>%
complete(nesting(group, subid), mem_conditions, fill=list(n=0))
sub_list = drt %>% filter(n < 6) %>% ungroup %>% pull(subid) %>% unique()
sub_list## [1] ap151 ap156
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174# look at mean trial counts/cond/group
drt %>%
filter(!subid %in% sub_list) %>% # need at least 6 trials/bin
group_by(group, mem_conditions) %>%
summarise(mean=mean(n), sd=sd(n), n=n())## Source: local data frame [2 x 5]
## Groups: group [?]
##
## # A tibble: 2 x 5
## group mem_conditions mean sd n
## <fctr> <fctr> <dbl> <dbl> <int>
## 1 control-fmri sourcehit 81.04348 38.57163 23
## 2 stress-fmri sourcehit 52.54545 23.32938 22# get medians/sds
head(dt)## X run trial onset duration shockCond shockTrial target associate
## 1 0 1 1 12.0191 10.6661 safe 0 BANDIT cemetery
## 2 1 1 2 22.7129 9.1242 safe 0 WALRUS foil
## 3 2 1 3 31.8642 9.4224 safe 0 VIOLIN throne_room
## 4 3 1 4 41.3132 11.3389 safe 0 MEDAL hayfield
## 5 4 1 5 52.6786 9.7644 safe 0 MANURE canyon
## 6 5 1 6 62.4701 9.1180 safe 0 SARDINE mountain_snowy
## resp acc accSpec respRT subid group remove anxious happy safe
## 1 indoor SM SMO_Hi 2.1094 ap100 control-fmri NA 3 2 3
## 2 foil CR CR 1.7773 ap100 control-fmri NA 3 2 3
## 3 indoor H HI_Hi 1.6804 ap100 control-fmri NA 3 2 3
## 4 outdoor H HO_Hi 1.7268 ap100 control-fmri NA 3 2 3
## 5 outdoor H HO_Hi 1.3734 ap100 control-fmri NA 3 2 3
## 6 outdoor H HO_Hi 2.1476 ap100 control-fmri NA 3 2 3
## stressed life_stress sleep cond_orig cond reps shock_and_post conf
## 1 3 4 4.5 TO_4 outdoor 4 0 Hi
## 2 3 4 4.5 F_0 foil 0 0 N
## 3 3 4 4.5 TI_2 indoor 2 0 Hi
## 4 3 4 4.5 TO_2 outdoor 2 0 Hi
## 5 3 4 4.5 TO_2 outdoor 2 0 Hi
## 6 3 4 4.5 TO_4 outdoor 4 0 Hi
## onset_adj mem_conditions obj_conditions onset_code rt_z
## 1 0.0191 sourcemiss_hi old itemhit -1.20388009
## 2 10.7129 CR new CR -0.12867526
## 3 19.8642 sourcehit old sourcehit -0.74601988
## 4 29.3132 sourcehit old sourcehit -0.67385952
## 5 40.6786 sourcehit old sourcehit -1.22346016
## 6 50.4701 sourcehit old sourcehit -0.01943975
## mem_conditions_byrep onset_code_byrep
## 1 sourcemiss_hi-4 itemhit-4
## 2 CR-0 CR-0
## 3 sourcehit-2 sourcehit-2
## 4 sourcehit-2 sourcehit-2
## 5 sourcehit-2 sourcehit-2
## 6 sourcehit-4 sourcehit-4drt = dt %>%
filter(mem_conditions %in% c("sourcehit"),
!subid %in% sub_list) %>%
group_by(group, subid, mem_conditions) %>%
summarise(rt=median(respRT), rt_sd = sd(respRT), n=n())
min(drt$n)## [1] 8drt %>% group_by(mem_conditions, group) %>% summarise(mean(rt), sd(rt), mean(rt_sd), n())## Source: local data frame [2 x 6]
## Groups: mem_conditions [?]
##
## # A tibble: 2 x 6
## mem_conditions group `mean(rt)` `sd(rt)` `mean(rt_sd)` `n()`
## <fctr> <fctr> <dbl> <dbl> <dbl> <int>
## 1 sourcehit control-fmri 2.221578 0.3557178 0.5505749 23
## 2 sourcehit stress-fmri 2.271007 0.3617676 0.5854840 22# median
bartlett.test(rt ~ group, data=drt %>% filter(mem_conditions == 'sourcehit'))##
## Bartlett test of homogeneity of variances
##
## data: rt by group
## Bartlett's K-squared = 0.0059736, df = 1, p-value = 0.9384t.test(rt ~ group, data=drt %>% filter(mem_conditions == 'sourcehit'), var.equal=TRUE)##
## Two Sample t-test
##
## data: rt by group
## t = -0.4621, df = 43, p-value = 0.6463
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2651454 0.1662882
## sample estimates:
## mean in group control-fmri mean in group stress-fmri
## 2.221578 2.271007# sd
bartlett.test(rt_sd ~ group, data=drt %>% filter(mem_conditions == 'sourcehit'))##
## Bartlett test of homogeneity of variances
##
## data: rt_sd by group
## Bartlett's K-squared = 1.9735, df = 1, p-value = 0.1601t.test(rt_sd ~ group, data=drt %>% filter(mem_conditions == 'sourcehit'), var.equal=TRUE)##
## Two Sample t-test
##
## data: rt_sd by group
## t = -1.0962, df = 43, p-value = 0.2791
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.09913017 0.02931212
## sample estimates:
## mean in group control-fmri mean in group stress-fmri
## 0.5505749 0.5854840RT: HC assoc hits vs. CRs
Calculate median/SDs:
## [1] ap151 ap156
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174## Source: local data frame [4 x 5]
## Groups: group [?]
##
## # A tibble: 4 x 5
## group mem_conditions mean sd n
## <fctr> <fctr> <dbl> <dbl> <int>
## 1 control-fmri CR 55.43478 20.08805 23
## 2 control-fmri sourcehit 81.04348 38.57163 23
## 3 stress-fmri CR 51.77273 17.56331 22
## 4 stress-fmri sourcehit 52.54545 23.32938 22## X run trial onset duration shockCond shockTrial target associate
## 1 0 1 1 12.0191 10.6661 safe 0 BANDIT cemetery
## 2 1 1 2 22.7129 9.1242 safe 0 WALRUS foil
## 3 2 1 3 31.8642 9.4224 safe 0 VIOLIN throne_room
## 4 3 1 4 41.3132 11.3389 safe 0 MEDAL hayfield
## 5 4 1 5 52.6786 9.7644 safe 0 MANURE canyon
## 6 5 1 6 62.4701 9.1180 safe 0 SARDINE mountain_snowy
## resp acc accSpec respRT subid group remove anxious happy safe
## 1 indoor SM SMO_Hi 2.1094 ap100 control-fmri NA 3 2 3
## 2 foil CR CR 1.7773 ap100 control-fmri NA 3 2 3
## 3 indoor H HI_Hi 1.6804 ap100 control-fmri NA 3 2 3
## 4 outdoor H HO_Hi 1.7268 ap100 control-fmri NA 3 2 3
## 5 outdoor H HO_Hi 1.3734 ap100 control-fmri NA 3 2 3
## 6 outdoor H HO_Hi 2.1476 ap100 control-fmri NA 3 2 3
## stressed life_stress sleep cond_orig cond reps shock_and_post conf
## 1 3 4 4.5 TO_4 outdoor 4 0 Hi
## 2 3 4 4.5 F_0 foil 0 0 N
## 3 3 4 4.5 TI_2 indoor 2 0 Hi
## 4 3 4 4.5 TO_2 outdoor 2 0 Hi
## 5 3 4 4.5 TO_2 outdoor 2 0 Hi
## 6 3 4 4.5 TO_4 outdoor 4 0 Hi
## onset_adj mem_conditions obj_conditions onset_code rt_z
## 1 0.0191 sourcemiss_hi old itemhit -1.20388009
## 2 10.7129 CR new CR -0.12867526
## 3 19.8642 sourcehit old sourcehit -0.74601988
## 4 29.3132 sourcehit old sourcehit -0.67385952
## 5 40.6786 sourcehit old sourcehit -1.22346016
## 6 50.4701 sourcehit old sourcehit -0.01943975
## mem_conditions_byrep onset_code_byrep
## 1 sourcemiss_hi-4 itemhit-4
## 2 CR-0 CR-0
## 3 sourcehit-2 sourcehit-2
## 4 sourcehit-2 sourcehit-2
## 5 sourcehit-2 sourcehit-2
## 6 sourcehit-4 sourcehit-4Median RT: SH vs. CR
contrasts(drt$group) = c(1,-1); contrasts(drt$group)## [,1]
## control-fmri 1
## stress-fmri -1contrasts(drt$mem_conditions) = c(-1,1); contrasts(drt$mem_conditions)## [,1]
## CR -1
## sourcehit 1fit = lmer(rt ~ group * mem_conditions + (1|subid), data=drt, REML=TRUE) # not enough obs for RE
# plot(fit) # these look fine, not log transforming rt
# hist(resid(fit))
summary(fit)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: rt ~ group * mem_conditions + (1 | subid)
## Data: drt
##
## REML criterion at convergence: 73.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7336 -0.4366 -0.0984 0.3634 3.7289
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.05504 0.2346
## Residual 0.06917 0.2630
## Number of obs: 90, groups: subid, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.09345 0.04464 43.00000 46.897 < 2e-16 ***
## group1 0.02324 0.04464 43.00000 0.521 0.6053
## mem_conditions1 0.15284 0.02773 43.00000 5.512 1.87e-06 ***
## group1:mem_conditions1 -0.04796 0.02773 43.00000 -1.729 0.0909 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 mm_cn1
## group1 -0.022
## mem_cndtns1 0.000 0.000
## grp1:mm_cn1 0.000 0.000 -0.022drt %>% group_by(mem_conditions, group) %>% summarise(mean(rt), sd(rt), n())## Source: local data frame [4 x 5]
## Groups: mem_conditions [?]
##
## # A tibble: 4 x 5
## mem_conditions group `mean(rt)` `sd(rt)` `n()`
## <fctr> <fctr> <dbl> <dbl> <int>
## 1 CR control-fmri 2.011815 0.3419605 23
## 2 CR stress-fmri 1.869418 0.3503218 22
## 3 sourcehit control-fmri 2.221578 0.3557178 23
## 4 sourcehit stress-fmri 2.271007 0.3617676 22drt %>% group_by(mem_conditions) %>% summarise(mean(rt), sd(rt), n())## # A tibble: 2 x 4
## mem_conditions `mean(rt)` `sd(rt)` `n()`
## <fctr> <dbl> <dbl> <int>
## 1 CR 1.942199 0.3496053 45
## 2 sourcehit 2.245743 0.3554651 45bartlett.test(rt ~ group, data=drt %>% filter(mem_conditions == 'sourcehit'))##
## Bartlett test of homogeneity of variances
##
## data: rt by group
## Bartlett's K-squared = 0.0059736, df = 1, p-value = 0.9384t.test(rt ~ group, data=drt %>% filter(mem_conditions == 'sourcehit'), var.equal=TRUE)##
## Two Sample t-test
##
## data: rt by group
## t = -0.4621, df = 43, p-value = 0.6463
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2651454 0.1662882
## sample estimates:
## mean in group control-fmri mean in group stress-fmri
## 2.221578 2.271007bartlett.test(rt ~ group, data=drt %>% filter(mem_conditions == 'CR'))##
## Bartlett test of homogeneity of variances
##
## data: rt by group
## Bartlett's K-squared = 0.012258, df = 1, p-value = 0.9118t.test(rt ~ group, data=drt %>% filter(mem_conditions == 'CR'), var.equal=TRUE)##
## Two Sample t-test
##
## data: rt by group
## t = 1.3798, df = 43, p-value = 0.1748
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.06573243 0.35052650
## sample estimates:
## mean in group control-fmri mean in group stress-fmri
## 2.011815 1.869418t.test(rt ~ mem_conditions, data=drt %>% filter(group == 'control-fmri'),
var.equal=TRUE, paired=TRUE)##
## Paired t-test
##
## data: rt by mem_conditions
## t = -4.3337, df = 22, p-value = 0.000267
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3101443 -0.1093818
## sample estimates:
## mean of the differences
## -0.209763t.test(rt ~ mem_conditions, data=drt %>% filter(group == 'stress-fmri'),
var.equal=TRUE, paired=TRUE)##
## Paired t-test
##
## data: rt by mem_conditions
## t = -3.9552, df = 21, p-value = 0.0007231
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.6127415 -0.1904358
## sample estimates:
## mean of the differences
## -0.4015886SD RT: SH vs. CR
contrasts(drt$group) = c(1,-1); contrasts(drt$group)## [,1]
## control-fmri 1
## stress-fmri -1contrasts(drt$mem_conditions) = c(-1,1); contrasts(drt$mem_conditions)## [,1]
## CR -1
## sourcehit 1head(drt)## Source: local data frame [6 x 6]
## Groups: group, subid [3]
##
## # A tibble: 6 x 6
## group subid mem_conditions rt rt_sd n
## <fctr> <fctr> <fctr> <dbl> <dbl> <int>
## 1 control-fmri ap100 CR 1.8108 0.4384731 63
## 2 control-fmri ap100 sourcehit 2.0958 0.6430124 89
## 3 control-fmri ap101 CR 2.2218 0.5597954 82
## 4 control-fmri ap101 sourcehit 1.9797 0.5214043 105
## 5 control-fmri ap102 CR 1.8402 0.6555323 23
## 6 control-fmri ap102 sourcehit 1.9995 0.5680501 68# hist(d$rt_sd)
fit = lmer(rt_sd ~ group * mem_conditions + (1|subid), data=drt, REML=TRUE) # not enough obs for RE
# plot(fit)
# hist(resid(fit))
summary(fit)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: rt_sd ~ group * mem_conditions + (1 | subid)
## Data: drt
##
## REML criterion at convergence: -90.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.91159 -0.55877 -0.02644 0.55570 2.64843
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.005684 0.07539
## Residual 0.011864 0.10892
## Number of obs: 90, groups: subid, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.582750 0.016070 43.000000 36.262 <2e-16 ***
## group1 -0.010910 0.016070 43.000000 -0.679 0.501
## mem_conditions1 -0.014720 0.011484 43.000000 -1.282 0.207
## group1:mem_conditions1 -0.006545 0.011484 43.000000 -0.570 0.572
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 mm_cn1
## group1 -0.022
## mem_cndtns1 0.000 0.000
## grp1:mm_cn1 0.000 0.000 -0.022drt %>% group_by(mem_conditions, group) %>% summarise(mean(rt_sd), sd(rt_sd), n())## Source: local data frame [4 x 5]
## Groups: mem_conditions [?]
##
## # A tibble: 4 x 5
## mem_conditions group `mean(rt_sd)` `sd(rt_sd)` `n()`
## <fctr> <fctr> <dbl> <dbl> <int>
## 1 CR control-fmri 0.5931054 0.16281772 23
## 2 CR stress-fmri 0.6018348 0.14402301 22
## 3 sourcehit control-fmri 0.5505749 0.08972954 23
## 4 sourcehit stress-fmri 0.5854840 0.12212281 22bartlett.test(rt_sd ~ group, data=drt %>% filter(mem_conditions == 'sourcehit'))##
## Bartlett test of homogeneity of variances
##
## data: rt_sd by group
## Bartlett's K-squared = 1.9735, df = 1, p-value = 0.1601t.test(rt ~ group, data=drt %>% filter(mem_conditions == 'sourcehit'), var.equal=TRUE)##
## Two Sample t-test
##
## data: rt by group
## t = -0.4621, df = 43, p-value = 0.6463
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2651454 0.1662882
## sample estimates:
## mean in group control-fmri mean in group stress-fmri
## 2.221578 2.271007RT: HC assoc hit vs. item hits
Calculate median/SDs:
## [1] ap104 ap116 ap151 ap156 ap160 ap169
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174## Source: local data frame [4 x 5]
## Groups: group [?]
##
## # A tibble: 4 x 5
## group mem_conditions mean sd n
## <fctr> <fctr> <dbl> <dbl> <int>
## 1 control-fmri itemhit_lo 54.23810 31.25845 21
## 2 control-fmri sourcehit 74.71429 33.95312 21
## 3 stress-fmri itemhit_lo 67.10000 23.74846 20
## 4 stress-fmri sourcehit 51.70000 24.14016 20## [1] 8Median RT: SH vs. IH
contrasts(d$group) = c(1,-1); contrasts(d$group)## [,1]
## control-fmri 1
## stress-fmri -1contrasts(d$mem_conditions) = c(1,-1); contrasts(d$mem_conditions)## [,1]
## itemhit_lo 1
## sourcehit -1fit = lmer(rt ~ group * mem_conditions + (1|subid), data=d, REML=TRUE) # not enough obs for RE
summary(fit)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: rt ~ group * mem_conditions + (1 | subid)
## Data: d
##
## REML criterion at convergence: 94.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.15494 -0.51267 0.06231 0.38446 2.52689
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.08562 0.2926
## Residual 0.09296 0.3049
## Number of obs: 82, groups: subid, 41
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.38756 0.05678 39.00000 42.049 < 2e-16 ***
## group1 0.02222 0.05678 39.00000 0.391 0.69770
## mem_conditions1 0.11740 0.03368 39.00000 3.486 0.00123 **
## group1:mem_conditions1 0.04161 0.03368 39.00000 1.235 0.22404
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 mm_cn1
## group1 -0.024
## mem_cndtns1 0.000 0.000
## grp1:mm_cn1 0.000 0.000 -0.024d %>% group_by(group, mem_conditions) %>% summarise(mean(rt), sd(rt), n())## Source: local data frame [4 x 5]
## Groups: group [?]
##
## # A tibble: 4 x 5
## group mem_conditions `mean(rt)` `sd(rt)` `n()`
## <fctr> <fctr> <dbl> <dbl> <int>
## 1 control-fmri itemhit_lo 2.568783 0.5596197 21
## 2 control-fmri sourcehit 2.250767 0.3590183 21
## 3 stress-fmri itemhit_lo 2.441125 0.3633611 20
## 4 stress-fmri sourcehit 2.289553 0.3684635 20d %>% group_by(mem_conditions) %>% summarise(mean(rt), sd(rt), n())## # A tibble: 2 x 4
## mem_conditions `mean(rt)` `sd(rt)` `n()`
## <fctr> <dbl> <dbl> <int>
## 1 itemhit_lo 2.506511 0.4727320 41
## 2 sourcehit 2.269687 0.3596121 41SD RT: SH vs. CR
contrasts(d$mem_conditions)## [,1]
## itemhit_lo 1
## sourcehit -1fit = lmer(rt_sd ~ group * mem_conditions + (1|subid), data=d, REML=TRUE) # not enough obs for RE
summary(fit)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: rt_sd ~ group * mem_conditions + (1 | subid)
## Data: d
##
## REML criterion at convergence: -107.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5836 -0.5429 -0.1018 0.5050 2.0233
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.009172 0.09577
## Residual 0.005804 0.07618
## Number of obs: 82, groups: subid, 41
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.550156 0.017165 39.000000 32.050 <2e-16 ***
## group1 -0.020211 0.017165 39.000000 -1.177 0.2462
## mem_conditions1 -0.018688 0.008416 39.000000 -2.221 0.0323 *
## group1:mem_conditions1 -0.004962 0.008416 39.000000 -0.590 0.5588
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 mm_cn1
## group1 -0.024
## mem_cndtns1 0.000 0.000
## grp1:mm_cn1 0.000 0.000 -0.024# anova(fit)RT: HC assoc hits by encoding strength
Calculate median/SDs:
## [1] ap110 ap158 ap151 ap156 ap173
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174## Source: local data frame [4 x 5]
## Groups: group [?]
##
## # A tibble: 4 x 5
## group reps mean sd n
## <fctr> <int> <dbl> <dbl> <int>
## 1 control-fmri 2 36.42857 17.18887 21
## 2 control-fmri 4 50.85714 17.88655 21
## 3 stress-fmri 2 21.42857 10.37098 21
## 4 stress-fmri 4 33.23810 12.29595 21## X run trial onset duration shockCond shockTrial target associate
## 1 0 1 1 12.0191 10.6661 safe 0 BANDIT cemetery
## 2 1 1 2 22.7129 9.1242 safe 0 WALRUS foil
## 3 2 1 3 31.8642 9.4224 safe 0 VIOLIN throne_room
## 4 3 1 4 41.3132 11.3389 safe 0 MEDAL hayfield
## 5 4 1 5 52.6786 9.7644 safe 0 MANURE canyon
## 6 5 1 6 62.4701 9.1180 safe 0 SARDINE mountain_snowy
## resp acc accSpec respRT subid group remove anxious happy safe
## 1 indoor SM SMO_Hi 2.1094 ap100 control-fmri NA 3 2 3
## 2 foil CR CR 1.7773 ap100 control-fmri NA 3 2 3
## 3 indoor H HI_Hi 1.6804 ap100 control-fmri NA 3 2 3
## 4 outdoor H HO_Hi 1.7268 ap100 control-fmri NA 3 2 3
## 5 outdoor H HO_Hi 1.3734 ap100 control-fmri NA 3 2 3
## 6 outdoor H HO_Hi 2.1476 ap100 control-fmri NA 3 2 3
## stressed life_stress sleep cond_orig cond reps shock_and_post conf
## 1 3 4 4.5 TO_4 outdoor 4 0 Hi
## 2 3 4 4.5 F_0 foil 0 0 N
## 3 3 4 4.5 TI_2 indoor 2 0 Hi
## 4 3 4 4.5 TO_2 outdoor 2 0 Hi
## 5 3 4 4.5 TO_2 outdoor 2 0 Hi
## 6 3 4 4.5 TO_4 outdoor 4 0 Hi
## onset_adj mem_conditions obj_conditions onset_code rt_z
## 1 0.0191 sourcemiss_hi old itemhit -1.20388009
## 2 10.7129 CR new CR -0.12867526
## 3 19.8642 sourcehit old sourcehit -0.74601988
## 4 29.3132 sourcehit old sourcehit -0.67385952
## 5 40.6786 sourcehit old sourcehit -1.22346016
## 6 50.4701 sourcehit old sourcehit -0.01943975
## mem_conditions_byrep onset_code_byrep
## 1 sourcemiss_hi-4 itemhit-4
## 2 CR-0 CR-0
## 3 sourcehit-2 sourcehit-2
## 4 sourcehit-2 sourcehit-2
## 5 sourcehit-2 sourcehit-2
## 6 sourcehit-4 sourcehit-4## [1] 7Median RT
contrasts(d$group) = c(1,-1); contrasts(d$group)## [,1]
## control-fmri 1
## stress-fmri -1contrasts(d$reps) = c(1,-1); contrasts(d$reps)## [,1]
## 2 1
## 4 -1fit = lmer(rt ~ group * reps + (1|subid), data=d, REML=TRUE) # not enough obs for RE
summary(fit)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: rt ~ group * reps + (1 | subid)
## Data: d
##
## REML criterion at convergence: 20.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.14369 -0.38865 -0.00463 0.40178 1.77725
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.08755 0.2959
## Residual 0.01901 0.1379
## Number of obs: 84, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.21325 0.04807 40.00000 46.041 < 2e-16 ***
## group1 -0.01536 0.04807 40.00000 -0.320 0.750963
## reps1 0.06079 0.01504 40.00000 4.041 0.000235 ***
## group1:reps1 0.01384 0.01504 40.00000 0.920 0.362942
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 reps1
## group1 0.000
## reps1 0.000 0.000
## group1:rps1 0.000 0.000 0.000d %>% group_by(reps) %>% summarise(mean(rt), sd(rt), n())## # A tibble: 2 x 4
## reps `mean(rt)` `sd(rt)` `n()`
## <fctr> <dbl> <dbl> <int>
## 1 2 2.274038 0.3246587 42
## 2 4 2.152455 0.3215515 42SD RT
fit = lmer(rt_sd ~ group * reps + (1|subid), data=d, REML=TRUE) # not enough obs for RE
summary(fit)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: rt_sd ~ group * reps + (1 | subid)
## Data: d
##
## REML criterion at convergence: -122.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.77428 -0.56544 0.04429 0.44400 1.94456
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.005719 0.07562
## Residual 0.005967 0.07724
## Number of obs: 84, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.550051 0.014394 40.000000 38.213 <2e-16 ***
## group1 -0.012048 0.014394 40.000000 -0.837 0.408
## reps1 0.014083 0.008428 40.000000 1.671 0.103
## group1:reps1 0.002854 0.008428 40.000000 0.339 0.737
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 reps1
## group1 0.000
## reps1 0.000 0.000
## group1:rps1 0.000 0.000 0.000# anova(fit)RT: HC assoc hit - by run type
# Determine subs to toss due to low trial counts
drt = dt %>%
filter(mem_conditions %in% c("sourcehit")) %>%
group_by(group, subid, shockCond) %>%
summarise(n=n())%>%
ungroup() %>%
complete(nesting(group, subid), shockCond, fill=list(n=0))
sub_list = drt %>% filter(n < 6) %>% ungroup %>% pull(subid) %>% unique()
sub_list## [1] ap158 ap151 ap156 ap173
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174# look at mean trial counts/cond/group
drt %>%
filter(!subid %in% sub_list) %>% # need at least 6 trials/bin
group_by(group, shockCond) %>%
summarise(mean=mean(n), sd=sd(n), n=n())## Source: local data frame [4 x 5]
## Groups: group [?]
##
## # A tibble: 4 x 5
## group shockCond mean sd n
## <fctr> <fctr> <dbl> <dbl> <int>
## 1 control-fmri safe 42.54545 18.17567 22
## 2 control-fmri threat 41.68182 18.44009 22
## 3 stress-fmri safe 28.09524 10.89452 21
## 4 stress-fmri threat 26.57143 11.58262 21# get medians/sds
head(dt)## X run trial onset duration shockCond shockTrial target associate
## 1 0 1 1 12.0191 10.6661 safe 0 BANDIT cemetery
## 2 1 1 2 22.7129 9.1242 safe 0 WALRUS foil
## 3 2 1 3 31.8642 9.4224 safe 0 VIOLIN throne_room
## 4 3 1 4 41.3132 11.3389 safe 0 MEDAL hayfield
## 5 4 1 5 52.6786 9.7644 safe 0 MANURE canyon
## 6 5 1 6 62.4701 9.1180 safe 0 SARDINE mountain_snowy
## resp acc accSpec respRT subid group remove anxious happy safe
## 1 indoor SM SMO_Hi 2.1094 ap100 control-fmri NA 3 2 3
## 2 foil CR CR 1.7773 ap100 control-fmri NA 3 2 3
## 3 indoor H HI_Hi 1.6804 ap100 control-fmri NA 3 2 3
## 4 outdoor H HO_Hi 1.7268 ap100 control-fmri NA 3 2 3
## 5 outdoor H HO_Hi 1.3734 ap100 control-fmri NA 3 2 3
## 6 outdoor H HO_Hi 2.1476 ap100 control-fmri NA 3 2 3
## stressed life_stress sleep cond_orig cond reps shock_and_post conf
## 1 3 4 4.5 TO_4 outdoor 4 0 Hi
## 2 3 4 4.5 F_0 foil 0 0 N
## 3 3 4 4.5 TI_2 indoor 2 0 Hi
## 4 3 4 4.5 TO_2 outdoor 2 0 Hi
## 5 3 4 4.5 TO_2 outdoor 2 0 Hi
## 6 3 4 4.5 TO_4 outdoor 4 0 Hi
## onset_adj mem_conditions obj_conditions onset_code rt_z
## 1 0.0191 sourcemiss_hi old itemhit -1.20388009
## 2 10.7129 CR new CR -0.12867526
## 3 19.8642 sourcehit old sourcehit -0.74601988
## 4 29.3132 sourcehit old sourcehit -0.67385952
## 5 40.6786 sourcehit old sourcehit -1.22346016
## 6 50.4701 sourcehit old sourcehit -0.01943975
## mem_conditions_byrep onset_code_byrep
## 1 sourcemiss_hi-4 itemhit-4
## 2 CR-0 CR-0
## 3 sourcehit-2 sourcehit-2
## 4 sourcehit-2 sourcehit-2
## 5 sourcehit-2 sourcehit-2
## 6 sourcehit-4 sourcehit-4drt = dt %>%
filter(mem_conditions %in% c("sourcehit"),
!subid %in% sub_list) %>%
group_by(group, subid, shockCond) %>%
summarise(rt=median(respRT), rt_sd = sd(respRT), n=n()) %>%
ungroup() %>%
mutate(shockCond = factor(shockCond))
drt## # A tibble: 86 x 6
## group subid shockCond rt rt_sd n
## <fctr> <fctr> <fctr> <dbl> <dbl> <int>
## 1 control-fmri ap100 safe 2.09785 0.5652564 46
## 2 control-fmri ap100 threat 2.03720 0.7217674 43
## 3 control-fmri ap101 safe 2.00345 0.5399894 54
## 4 control-fmri ap101 threat 1.89870 0.5025196 51
## 5 control-fmri ap102 safe 2.06535 0.5593426 34
## 6 control-fmri ap102 threat 1.93060 0.5694347 34
## 7 control-fmri ap103 safe 2.95525 0.6363623 56
## 8 control-fmri ap103 threat 3.06230 0.6758489 57
## 9 control-fmri ap104 safe 1.95380 0.5650548 75
## 10 control-fmri ap104 threat 1.83455 0.5149647 72
## # ... with 76 more rowsmin(drt$n)## [1] 7contrasts(drt$group) = c(1,-1); contrasts(drt$group)## [,1]
## control-fmri 1
## stress-fmri -1contrasts(drt$shockCond) = c(1,-1); contrasts(drt$shockCond)## [,1]
## safe 1
## threat -1bartlett.test(rt ~ shockCond, data=drt %>% filter(group == 'stress-fmri'))##
## Bartlett test of homogeneity of variances
##
## data: rt by shockCond
## Bartlett's K-squared = 0.033052, df = 1, p-value = 0.8557t.test(rt ~ shockCond, data=drt %>% filter(group == 'stress-fmri'),
paired=TRUE, var.equal=TRUE)##
## Paired t-test
##
## data: rt by shockCond
## t = 2.0394, df = 20, p-value = 0.05484
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.002008068 0.177922354
## sample estimates:
## mean of the differences
## 0.08795714drt %>% group_by(group, shockCond) %>% summarise(n())## Source: local data frame [4 x 3]
## Groups: group [?]
##
## # A tibble: 4 x 3
## group shockCond `n()`
## <fctr> <fctr> <int>
## 1 control-fmri safe 22
## 2 control-fmri threat 22
## 3 stress-fmri safe 21
## 4 stress-fmri threat 21bartlett.test(rt ~ group, data=drt %>% filter(shockCond == 'threat'))##
## Bartlett test of homogeneity of variances
##
## data: rt by group
## Bartlett's K-squared = 1.192, df = 1, p-value = 0.2749t.test(rt ~ group, data=drt %>% filter(shockCond == 'threat'),
paired=FALSE, var.equal=TRUE)##
## Two Sample t-test
##
## data: rt by group
## t = 0.2374, df = 41, p-value = 0.8135
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1783869 0.2259126
## sample estimates:
## mean in group control-fmri mean in group stress-fmri
## 2.198680 2.174917summary(lmer(rt ~ group * shockCond + (1 | subid), data=drt))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: rt ~ group * shockCond + (1 | subid)
## Data: drt
##
## REML criterion at convergence: 14.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.08156 -0.45837 -0.02917 0.28955 2.06907
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.09466 0.3077
## Residual 0.01549 0.1245
## Number of obs: 86, groups: subid, 43
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.21627 0.04881 41.00000 45.401 <2e-16 ***
## group1 -0.00262 0.04881 41.00000 -0.054 0.9575
## shockCond1 0.02948 0.01342 41.00000 2.196 0.0338 *
## group1:shockCond1 -0.01450 0.01342 41.00000 -1.080 0.2863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 shckC1
## group1 -0.023
## shockCond1 0.000 0.000
## grp1:shckC1 0.000 0.000 -0.023bartlett.test(rt_sd ~ shockCond, data=drt %>% filter(group == 'stress-fmri'))##
## Bartlett test of homogeneity of variances
##
## data: rt_sd by shockCond
## Bartlett's K-squared = 0.071461, df = 1, p-value = 0.7892t.test(rt_sd ~ shockCond, data=drt %>% filter(group == 'stress-fmri'),
paired=TRUE, var.equal=TRUE)##
## Paired t-test
##
## data: rt_sd by shockCond
## t = 0.17199, df = 20, p-value = 0.8652
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.06549640 0.07726781
## sample estimates:
## mean of the differences
## 0.005885704bartlett.test(rt_sd ~ group, data=drt %>% filter(shockCond == 'threat'))##
## Bartlett test of homogeneity of variances
##
## data: rt_sd by group
## Bartlett's K-squared = 4.4922, df = 1, p-value = 0.03405t.test(rt_sd ~ group, data=drt %>% filter(shockCond == 'threat'),
paired=FALSE, var.equal=TRUE)##
## Two Sample t-test
##
## data: rt_sd by group
## t = -0.60418, df = 41, p-value = 0.5491
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08703793 0.04695245
## sample estimates:
## mean in group control-fmri mean in group stress-fmri
## 0.5371164 0.5571591summary(lmer(rt_sd ~ group * shockCond + (1 | subid), data=drt))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: rt_sd ~ group * shockCond + (1 | subid)
## Data: drt
##
## REML criterion at convergence: -108.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.16712 -0.51203 0.00414 0.53979 1.80289
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.003345 0.05784
## Residual 0.009685 0.09841
## Number of obs: 86, groups: subid, 43
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.5491875 0.0138027 41.0000000 39.788 <2e-16 ***
## group1 -0.0109145 0.0138027 41.0000000 -0.791 0.434
## shockCond1 0.0020498 0.0106150 41.0000000 0.193 0.848
## group1:shockCond1 -0.0008931 0.0106150 41.0000000 -0.084 0.933
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 shckC1
## group1 -0.023
## shockCond1 0.000 0.000
## grp1:shckC1 0.000 0.000 -0.0232) Trial-wise analyses
Note that all these analyses exclude the 2 participants with poor VTC localizer classification:
Localizer classification:
VTC F1 score
d = read.csv('/Users/sgagnon/Dropbox/Stanford/Presentations/AP/mvpa_f1_localizer_group_scalewithinrun.csv')
str(d)## 'data.frame': 132 obs. of 8 variables:
## $ X : int 0 2 4 6 8 10 12 14 16 18 ...
## $ category : Factor w/ 3 levels "face","object",..: 1 2 3 1 2 3 1 2 3 1 ...
## $ mask_name: Factor w/ 1 level "bilat-parahipp_fusi_inftemp": 1 1 1 1 1 1 1 1 1 1 ...
## $ mean : num 0.949 0.84 0.875 0.921 0.859 ...
## $ sd : num 0.0349 0.0127 0.0279 0.0325 0.02 ...
## $ subid : Factor w/ 44 levels "ap100","ap101",..: 1 1 1 2 2 2 3 3 3 4 ...
## $ type : Factor w/ 1 level "f1 score": 1 1 1 1 1 1 1 1 1 1 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...# mean +/- SD
d %>%
group_by(subid, category) %>%
summarise(mean = mean(mean)) %>%
group_by(category) %>%
summarise(sd=sd(mean), f1 = mean(mean), n=n())## # A tibble: 3 x 4
## category sd f1 n
## <fctr> <dbl> <dbl> <int>
## 1 face 0.05242748 0.8864267 44
## 2 object 0.08163720 0.8155268 44
## 3 place 0.06588833 0.8663450 44# how are individ subjs doing? look for outliers
d_avg = d %>%
group_by(subid, group) %>%
summarise(f1 = mean(mean))
head(d_avg)## Source: local data frame [6 x 3]
## Groups: subid [6]
##
## # A tibble: 6 x 3
## subid group f1
## <fctr> <fctr> <dbl>
## 1 ap100 control 0.8882706
## 2 ap101 control 0.8928758
## 3 ap102 control 0.8817173
## 4 ap103 control 0.8463258
## 5 ap104 control 0.8143320
## 6 ap105 control 0.8602789boxplot(d_avg$f1)
boxplot.stats(d_avg$f1)## $stats
## [1] 0.7898834 0.8413915 0.8581023 0.8874281 0.9542282
##
## $n
## [1] 44
##
## $conf
## [1] 0.8471366 0.8690679
##
## $out
## [1] 0.6087732 0.7650795outlier_values <- boxplot.stats(d_avg$f1)$out
d_avg %>% filter(f1 %in% outlier_values)## Source: local data frame [2 x 3]
## Groups: subid [2]
##
## # A tibble: 2 x 3
## subid group f1
## <fctr> <fctr> <dbl>
## 1 ap168 stress 0.6087732
## 2 ap174 stress 0.7650795subids_rm = d_avg %>% filter(f1 %in% outlier_values) %>% collect %>% .[["subid"]]
subids_rm # ap168 ap174## [1] ap168 ap174
## 44 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ... ap174dim(d)## [1] 132 8d = d %>% filter(!subid %in% subids_rm)
dim(d)## [1] 126 8ggplot(d %>%
group_by(subid, group, category) %>%
summarise(f1 = mean(mean)), aes(x=category, y=f1, color=group)) +
geom_boxplot()+
geom_point(alpha=.7, size=3, position=position_jitterdodge()) +
ylab('F1 score')+
scale_color_manual(values=c('dodgerblue', 'orange')) +
xlab('') + ylim(.5, 1)
contrasts(d$group) = c(1,-1); contrasts(d$group)## [,1]
## control 1
## stress -1contrasts(d$category) = cbind(objVother=c(1,-2,1),
faceVplace=c(1,0,-1)); contrasts(d$category)## objVother faceVplace
## face 1 1
## object -2 0
## place 1 -1fit = lmer(mean ~ group*category + (1|subid), data=d, REML=TRUE)
anova(fit)## Analysis of Variance Table of type III with Satterthwaite
## approximation for degrees of freedom
## Sum Sq Mean Sq NumDF DenDF F.value Pr(>F)
## group 0.001310 0.001310 1 40.000 0.6345 0.4304
## category 0.084162 0.042081 2 80.001 20.3812 7.02e-08 ***
## group:category 0.007467 0.003734 2 80.001 1.8083 0.1706
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(fit)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: mean ~ group * category + (1 | subid)
## Data: d
##
## REML criterion at convergence: -348.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6125 -0.6483 0.1128 0.6217 1.7904
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.0005532 0.02352
## Residual 0.0020647 0.04544
## Number of obs: 126, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.863949 0.005443 40.000000 158.729 < 2e-16
## group1 0.004335 0.005443 40.000000 0.797 0.4304
## categoryobjVother 0.017879 0.002866 80.000000 6.239 1.97e-08
## categoryfaceVplace 0.006728 0.004963 80.000000 1.356 0.1791
## group1:categoryobjVother 0.005145 0.002866 80.000000 1.795 0.0764
## group1:categoryfaceVplace -0.003114 0.004963 80.000000 -0.627 0.5322
##
## (Intercept) ***
## group1
## categoryobjVother ***
## categoryfaceVplace
## group1:categoryobjVother .
## group1:categoryfaceVplace
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 ctgrybV ctgryfV grp1:ctgrybV
## group1 -0.048
## ctgrybjVthr 0.000 0.000
## ctgryfcVplc 0.000 0.000 0.000
## grp1:ctgrybV 0.000 0.000 -0.048 0.000
## grp1:ctgryfV 0.000 0.000 0.000 -0.048 0.000Inferior parietal F1 score
d = read.csv('/Users/sgagnon/Dropbox/Stanford/Presentations/AP/mvpa_f1_localizer_inferiorparietal_group_scalewithinrun.csv')
str(d)## 'data.frame': 132 obs. of 8 variables:
## $ X : int 0 2 4 6 8 10 12 14 16 18 ...
## $ category : Factor w/ 3 levels "face","object",..: 1 2 3 1 2 3 1 2 3 1 ...
## $ mask_name: Factor w/ 1 level "lh-inferiorparietal": 1 1 1 1 1 1 1 1 1 1 ...
## $ mean : num 0.837 0.638 0.822 0.891 0.85 ...
## $ sd : num 0.00825 0.01871 0.00193 0.02976 0.03865 ...
## $ subid : Factor w/ 44 levels "ap100","ap101",..: 1 1 1 2 2 2 3 3 3 4 ...
## $ type : Factor w/ 1 level "f1 score": 1 1 1 1 1 1 1 1 1 1 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...# mean +/- SD
d %>%
group_by(subid, category) %>%
summarise(mean = mean(mean)) %>%
group_by(category) %>%
summarise(sd=sd(mean), f1 = mean(mean), n=n())## # A tibble: 3 x 4
## category sd f1 n
## <fctr> <dbl> <dbl> <int>
## 1 face 0.07580574 0.7747272 44
## 2 object 0.10549956 0.7081117 44
## 3 place 0.06744105 0.8162107 44# how are individ subjs doing? look for outliers
d_avg = d %>%
group_by(subid, group) %>%
summarise(f1 = mean(mean))
head(d_avg)## Source: local data frame [6 x 3]
## Groups: subid [6]
##
## # A tibble: 6 x 3
## subid group f1
## <fctr> <fctr> <dbl>
## 1 ap100 control 0.7656174
## 2 ap101 control 0.8799442
## 3 ap102 control 0.8071681
## 4 ap103 control 0.6936104
## 5 ap104 control 0.7287970
## 6 ap105 control 0.7839734boxplot(d_avg$f1)
boxplot.stats(d_avg$f1)## $stats
## [1] 0.6618044 0.7327632 0.7570372 0.8029153 0.8985969
##
## $n
## [1] 44
##
## $conf
## [1] 0.7403274 0.7737471
##
## $out
## [1] 0.5318235outlier_values <- boxplot.stats(d_avg$f1)$out
d_avg %>% filter(f1 %in% outlier_values)## Source: local data frame [1 x 3]
## Groups: subid [1]
##
## # A tibble: 1 x 3
## subid group f1
## <fctr> <fctr> <dbl>
## 1 ap168 stress 0.5318235subids_rm = d_avg %>% filter(f1 %in% outlier_values) %>% collect %>% .[["subid"]]
subids_rm # ap168## [1] ap168
## 44 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ... ap174dim(d)## [1] 132 8d = d %>% filter(!subid %in% subids_rm)
dim(d)## [1] 129 8ggplot(d %>%
group_by(subid, group, category) %>%
summarise(f1 = mean(mean)), aes(x=category, y=f1, color=group)) +
geom_boxplot()+
geom_point(alpha=.7, size=3, position=position_jitterdodge()) +
ylab('F1 score')+
scale_color_manual(values=c('dodgerblue', 'orange')) +
xlab('') + ylim(.5, 1)
contrasts(d$group) = c(1,-1); contrasts(d$group)## [,1]
## control 1
## stress -1contrasts(d$category) = cbind(objVother=c(1,-2,1),
faceVplace=c(1,0,-1)); contrasts(d$category)## objVother faceVplace
## face 1 1
## object -2 0
## place 1 -1fit = lmer(mean ~ group*category + (1|subid), data=d, REML=TRUE)
anova(fit)## Analysis of Variance Table of type III with Satterthwaite
## approximation for degrees of freedom
## Sum Sq Mean Sq NumDF DenDF F.value Pr(>F)
## group 0.009481 0.009481 1 41 2.3455 0.1333
## category 0.227530 0.113765 2 82 28.1446 4.943e-10 ***
## group:category 0.001778 0.000889 2 82 0.2200 0.8030
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(fit)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: mean ~ group * category + (1 | subid)
## Data: d
##
## REML criterion at convergence: -266.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6684 -0.6006 0.1029 0.6387 1.9203
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.001602 0.04003
## Residual 0.004042 0.06358
## Number of obs: 129, groups: subid, 43
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.771509 0.008285 41.000000 93.126 < 2e-16
## group1 0.012688 0.008285 41.000000 1.532 0.1333
## categoryobjVother 0.027432 0.003959 82.000000 6.929 8.82e-10
## categoryfaceVplace -0.019737 0.006858 82.000000 -2.878 0.0051
## group1:categoryobjVother 0.001373 0.003959 82.000000 0.347 0.7296
## group1:categoryfaceVplace 0.003877 0.006858 82.000000 0.565 0.5734
##
## (Intercept) ***
## group1
## categoryobjVother ***
## categoryfaceVplace **
## group1:categoryobjVother
## group1:categoryfaceVplace
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 ctgrybV ctgryfV grp1:ctgrybV
## group1 -0.023
## ctgrybjVthr 0.000 0.000
## ctgryfcVplc 0.000 0.000 0.000
## grp1:ctgrybV 0.000 0.000 -0.023 0.000
## grp1:ctgryfV 0.000 0.000 0.000 -0.023 0.000Load in data
d = read.csv('/Users/sgagnon/Dropbox/Stanford/Presentations/AP/mvpa_logit_place_byreps_avg_46810_filtartloc_scalewithinrun.csv')
subids_rm = c('ap168', 'ap174') # bad VTC localizer classification
# filter d to just good subjs, and only OLD items
d = d %>%
filter(!subid %in% subids_rm) %>%
filter(cond %in% c('sourcemiss_hi', 'sourcehit', 'M', 'itemhit_lo')) %>%
mutate(pcorr = factor(ifelse(cond == "sourcehit", 1, 0)),
vtc_logit = avg_logit)
dt %>% filter(shock_and_post == 2) # one subj w/consecutive shocks - trial needs to be removed (though mem_condition == 'nuisance')## X run trial onset duration shockCond shockTrial target associate
## 1 6677 5 13 141.3117 11.5306 threat 1 WHIP ocean
## resp acc accSpec respRT subid group remove anxious happy safe
## 1 outdoor H HO_Hi 0.1927 ap160 stress-fmri NA 4 3 6
## stressed life_stress sleep cond_orig cond reps shock_and_post conf
## 1 5 3 4.5 TO_4 outdoor 4 2 Hi
## onset_adj mem_conditions obj_conditions onset_code rt_z
## 1 129.3117 nuisance nuisance nuisance -3.136573
## mem_conditions_byrep onset_code_byrep
## 1 nuisance-4 nuisance-4# Merge w/other behavioral info
d = dt %>%
mutate(onset=onset_adj, img_type=cond) %>%
dplyr::select(-group, -reps, -cond) %>%
right_join(d, by=c('subid', 'run', 'onset')) %>%
mutate(subid = factor(subid), imgType = factor(img_type))## Warning in right_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vectordim(d)## [1] 6624 50d = d %>% filter(shock_and_post == 0)
dim(d)## [1] 6623 50# Read in hipp BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-hippocampus.csv')
roi_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-hippocampus.csv')
# dim(roi_lh); dim(roi_rh)
# Take timepoints of interest for old trials, and collapse across hemispheres
roi_f = bind_rows("lh" = roi_lh, "rh" = roi_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, hemi, run, onset) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset) %>%
summarise(hipp_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character# Read in hipp tail BOLD (as potential secondary analysis)
roi_tail_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-hippocampus-tail.csv')
roi_tail_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-hippocampus-tail.csv')
# Take timepoints of interest for old trials, and collapse across hemispheres
roi_tail_f = bind_rows("lh" = roi_tail_lh, "rh" = roi_tail_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, hemi, run, onset) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset) %>%
summarise(hipp_tail_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character# Read in angular BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-DefaultA_IPL.csv')
angular_lh_f = roi_lh %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, run, onset) %>%
summarise(angular_lh_sig = mean_(mean_activity)) %>% ungroup()
# Read in RSP BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-DefaultC_Rsp.csv')
roi_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-DefaultC_Rsp.csv')
# Take timepoints of interest for old trials, and collapse across hemispheres
rsp_f = bind_rows("lh" = roi_lh, "rh" = roi_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, hemi, run, onset) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset) %>%
summarise(rsp_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character# Read in CCN BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-frontoparietal.csv')
roi_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-frontoparietal.csv')
# dim(roi_lh); dim(roi_rh)
# Take timepoints of interest for old trials, and collapse across hemispheres
CCN_f = bind_rows("lh" = roi_lh, "rh" = roi_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, hemi, run, onset) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset) %>%
summarise(CCN_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character# also, just for LH
CCN_lh_f = roi_lh %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, run, onset) %>%
summarise(CCN_lh_sig = mean_(mean_activity)) %>% ungroup()
# Read in DAN BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-dorsalattn.csv')
roi_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-dorsalattn.csv')
# dim(roi_lh); dim(roi_rh)
# Take timepoints of interest for old trials, and collapse across hemispheres
DAN_f = bind_rows("lh" = roi_lh, "rh" = roi_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, hemi, run, onset) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset) %>%
summarise(DAN_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character# also, just for LH
DAN_lh_f = roi_lh %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, run, onset) %>%
summarise(DAN_lh_sig = mean_(mean_activity)) %>% ungroup()
# Other reinstatement
d_infpar = read.csv('/Users/sgagnon/Dropbox/Stanford/Presentations/AP/mvpa_logit_inferiorparietal_place_byreps_avg_46810_filtartloc_scalewithinrun.csv') %>%
filter(!subid %in% subids_rm) %>%
filter(cond %in% c('sourcemiss_hi', 'sourcehit', 'M', 'itemhit_lo')) %>%
mutate(ang_logit = avg_logit) %>%
dplyr::select(subid, run, onset, ang_logit)
d_hipp = read.csv('/Users/sgagnon/Dropbox/Stanford/Presentations/AP/mvpa_logit_bilat-hippocampus_place_byreps_avg_46810_filtartloc_scalewithinrun.csv') %>%
filter(!subid %in% subids_rm) %>%
filter(cond %in% c('sourcemiss_hi', 'sourcehit', 'M', 'itemhit_lo')) %>%
mutate(pcorr = factor(ifelse(cond == "sourcehit", 1, 0)),
hipp_logit = avg_logit) %>%
dplyr::select(subid, run, onset, hipp_logit)
# to see if need phc (maybe includes "general memory" response?)
d_vtc_nophc = read.csv('/Users/sgagnon/Dropbox/Stanford/Presentations/AP/mvpa_logit_bilat-fusi_inftemp_place_byreps_avg_46810_filtartloc_scalewithinrun.csv') %>%
filter(!subid %in% subids_rm) %>%
filter(cond %in% c('sourcemiss_hi', 'sourcehit', 'M', 'itemhit_lo')) %>%
mutate(pcorr = factor(ifelse(cond == "sourcehit", 1, 0)),
vtc_nophc_logit = avg_logit) %>%
dplyr::select(subid, run, onset, vtc_nophc_logit)
# Merge w/VTC classifier evidence
dm = d %>%
left_join(roi_f, by=c('subid', 'run', 'onset')) %>%
left_join(roi_tail_f, by=c('subid', 'run', 'onset')) %>%
left_join(DAN_f, by=c('subid', 'run', 'onset')) %>%
left_join(CCN_f, by=c('subid', 'run', 'onset')) %>%
left_join(rsp_f, by=c('subid', 'run', 'onset')) %>%
left_join(angular_lh_f, by=c('subid', 'run', 'onset')) %>%
left_join(DAN_lh_f, by=c('subid', 'run', 'onset')) %>%
left_join(CCN_lh_f, by=c('subid', 'run', 'onset')) %>%
left_join(d_infpar, by=c('subid', 'run', 'onset')) %>%
left_join(d_hipp, by=c('subid', 'run', 'onset')) %>%
left_join(d_vtc_nophc, by=c('subid', 'run', 'onset')) %>%
group_by(subid) %>%
mutate(ang_logit_z = zscore(ang_logit),
vtc_logit_z = zscore(vtc_logit),
hipp_logit_z = zscore(hipp_logit),
CCN_sig_z = zscore(CCN_sig),
rsp_sig_z = zscore(rsp_sig),
DAN_sig_z = zscore(DAN_sig),
angular_lh_sig_z = zscore(angular_lh_sig),
CCN_lh_sig_z = zscore(CCN_lh_sig),
DAN_lh_sig_z = zscore(DAN_lh_sig),
hipp_sig_z = zscore(hipp_sig),
hipp_tail_sig_z = zscore(hipp_tail_sig),
reps = factor(reps),
hipp_quintile = ntile(hipp_sig, 5),
rsp_quintile = ntile(rsp_sig, 5),
CCN_quintile = ntile(CCN_sig, 5),
DAN_quintile = ntile(DAN_sig, 5),
angular_lh_sig_quintile = ntile(angular_lh_sig, 5),
CCN_lh_quintile = ntile(CCN_lh_sig, 5),
DAN_lh_quintile = ntile(DAN_lh_sig, 5),
hipp_tail_quintile = ntile(hipp_tail_sig, 5),
vtc_quintile = ntile(vtc_logit, 5),
hipp_logit_quintile = ntile(hipp_logit, 5),
ang_quintile = ntile(ang_logit, 5)) %>%
ungroup() %>%
mutate(subid = factor(subid),
cond = factor(cond),
condition = factor(condition))## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vector## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vectordim(dm) #just hipp: 6623 ## [1] 6623 83with(dm, table(subid, reps))## reps
## subid 2 4
## ap100 76 78
## ap101 83 82
## ap102 80 79
## ap103 83 79
## ap104 82 83
## ap105 81 81
## ap107 79 82
## ap108 72 74
## ap109 81 77
## ap110 82 81
## ap111 82 84
## ap113 83 83
## ap114 81 81
## ap115 78 82
## ap116 80 82
## ap117 84 80
## ap118 83 81
## ap119 80 81
## ap120 79 76
## ap121 84 81
## ap122 84 82
## ap150 77 78
## ap152 73 78
## ap153 77 75
## ap154 67 78
## ap155 69 66
## ap157 77 79
## ap158 84 83
## ap159 81 81
## ap160 66 64
## ap161 81 79
## ap162 75 80
## ap163 83 78
## ap164 79 79
## ap165 75 81
## ap166 79 79
## ap167 76 77
## ap169 77 79
## ap170 81 81
## ap171 81 81
## ap172 80 76
## ap173 73 74str(dm)## Classes 'tbl_df', 'tbl' and 'data.frame': 6623 obs. of 83 variables:
## $ X.x : int 0 2 3 4 5 7 8 11 14 15 ...
## $ run : num 1 1 1 1 1 1 1 1 1 1 ...
## $ trial : int 1 3 4 5 6 8 9 12 15 16 ...
## $ onset : num 0.0191 19.8642 29.3132 40.6786 50.4701 ...
## $ duration : num 10.67 9.42 11.34 9.76 9.12 ...
## $ shockCond : Factor w/ 2 levels "safe","threat": 1 1 1 1 1 1 1 1 1 1 ...
## $ shockTrial : int 0 0 0 0 0 0 0 0 0 0 ...
## $ target : Factor w/ 252 levels "ACCORDIAN","ACROBAT",..: 15 238 116 114 183 224 87 102 192 191 ...
## $ associate : Factor w/ 241 levels "\002\002","\002",..: 68 237 126 61 175 233 53 118 173 84 ...
## $ resp : Factor w/ 4 levels "foil","indoor",..: 2 2 4 4 4 1 1 1 4 2 ...
## $ acc : Factor w/ 6 levels "CR","FA","H",..: 6 3 3 3 3 4 4 4 3 3 ...
## $ accSpec : Factor w/ 15 levels "CR","FAI_Hi",..: 14 6 8 8 8 10 10 10 8 6 ...
## $ respRT : num 2.11 1.68 1.73 1.37 2.15 ...
## $ subid : Factor w/ 42 levels "ap100","ap101",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ remove : logi NA NA NA NA NA NA ...
## $ anxious : int 3 3 3 3 3 3 3 3 3 3 ...
## $ happy : int 2 2 2 2 2 2 2 2 2 2 ...
## $ safe : int 3 3 3 3 3 3 3 3 3 3 ...
## $ stressed : int 3 3 3 3 3 3 3 3 3 3 ...
## $ life_stress : int 4 4 4 4 4 4 4 4 4 4 ...
## $ sleep : num 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 ...
## $ cond_orig : Factor w/ 5 levels "F_0","TI_2","TI_4",..: 5 2 4 4 5 3 2 4 5 2 ...
## $ shock_and_post : int 0 0 0 0 0 0 0 0 0 0 ...
## $ conf : Factor w/ 3 levels "Hi","Lo","N": 1 1 1 1 1 3 3 3 1 1 ...
## $ onset_adj : num 0.0191 19.8642 29.3132 40.6786 50.4701 ...
## $ mem_conditions : Factor w/ 7 levels "CR","FA","itemhit_lo",..: 7 6 6 6 6 4 4 4 6 6 ...
## $ obj_conditions : Factor w/ 3 levels "new","nuisance",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ onset_code : Factor w/ 6 levels "CR","FA","itemhit",..: 3 6 6 6 6 4 4 4 6 6 ...
## $ rt_z : num -1.2039 -0.746 -0.6739 -1.2235 -0.0194 ...
## $ mem_conditions_byrep : Factor w/ 13 levels "CR-0","FA-0",..: 13 10 10 10 11 6 5 5 11 10 ...
## $ onset_code_byrep : Factor w/ 11 levels "CR-0","FA-0",..: 4 10 10 10 11 6 5 5 11 10 ...
## $ img_type : Factor w/ 3 levels "foil","indoor",..: 3 2 3 3 3 2 2 3 3 2 ...
## $ X.y : int 2 8 11 14 17 23 26 35 44 47 ...
## $ index : int 2 8 11 14 17 23 26 35 44 47 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## $ condition : Factor w/ 8 levels "itemhit_lo-2",..: 8 5 5 5 6 4 3 3 6 5 ...
## $ category : Factor w/ 1 level "place": 1 1 1 1 1 1 1 1 1 1 ...
## $ X0 : num -6.635 -9.265 6.804 8.431 0.203 ...
## $ X2 : num -6.32 -4.39 7.22 3.92 -3.54 ...
## $ X4 : num -1.49 5.79 4.22 1.8 -2.75 ...
## $ X6 : num 2.59 5.69 -3.03 3.7 3.74 ...
## $ X8 : num -1.03 8.65 -1.68 -1.28 -2.28 ...
## $ X10 : num -0.642 6.486 8.468 -0.23 -9.405 ...
## $ X12 : num -1.1 6.64 11.49 -3.55 -5.64 ...
## $ avg_logit : num -0.145 6.655 1.996 0.998 -2.673 ...
## $ cond : Factor w/ 4 levels "itemhit_lo","M",..: 4 3 3 3 3 2 2 2 3 3 ...
## $ reps : Factor w/ 2 levels "2","4": 2 1 1 1 2 2 1 1 2 1 ...
## $ pcorr : Factor w/ 2 levels "0","1": 1 2 2 2 2 1 1 1 2 2 ...
## $ vtc_logit : num -0.145 6.655 1.996 0.998 -2.673 ...
## $ imgType : Factor w/ 2 levels "indoor","outdoor": 2 1 2 2 2 1 1 2 2 1 ...
## $ hipp_sig : num 0.2754 0.4613 -0.1955 -0.0154 -0.14 ...
## $ hipp_tail_sig : num 0.11449 0.49252 -0.00556 -0.06895 -0.13196 ...
## $ DAN_sig : num 0.175 0.658 0.225 -0.262 -0.395 ...
## $ CCN_sig : num 0.367 0.4107 0.0192 -0.1273 -0.2068 ...
## $ rsp_sig : num 0.3053 1.5956 -0.0991 -0.19 -0.5123 ...
## $ angular_lh_sig : num 0.20058 0.85916 -0.09532 0.00574 -0.04293 ...
## $ DAN_lh_sig : num 0.212 0.585 0.183 -0.233 -0.383 ...
## $ CCN_lh_sig : num 0.3687 0.2566 0.1633 -0.1271 -0.0121 ...
## $ ang_logit : num -2.066 5.935 0.249 -3.792 -6.49 ...
## $ hipp_logit : num -2.553 1.418 -1.783 -2.571 -0.555 ...
## $ vtc_nophc_logit : num 0.249 6.553 1.733 0.751 -3.801 ...
## $ ang_logit_z : num -0.474 1.931 0.222 -0.993 -1.804 ...
## $ vtc_logit_z : num -0.148 1.895 0.495 0.195 -0.907 ...
## $ hipp_logit_z : num -0.491 1.406 -0.123 -0.499 0.464 ...
## $ CCN_sig_z : num 0.6436 0.7336 -0.0722 -0.3738 -0.5374 ...
## $ rsp_sig_z : num 0.209 2.071 -0.375 -0.506 -0.971 ...
## $ DAN_sig_z : num 0.391 1.606 0.516 -0.711 -1.046 ...
## $ angular_lh_sig_z : num 0.177 1.513 -0.423 -0.218 -0.317 ...
## $ CCN_lh_sig_z : num 0.537 0.31 0.121 -0.468 -0.235 ...
## $ DAN_lh_sig_z : num 0.437 1.389 0.363 -0.7 -1.082 ...
## $ hipp_sig_z : num 0.537 0.967 -0.552 -0.135 -0.424 ...
## $ hipp_tail_sig_z : num 0.167 1.146 -0.143 -0.307 -0.47 ...
## $ hipp_quintile : int 4 5 2 3 2 1 4 1 3 4 ...
## $ rsp_quintile : int 3 5 2 2 1 1 4 1 3 3 ...
## $ CCN_quintile : int 4 4 3 2 2 1 5 1 3 3 ...
## $ DAN_quintile : int 4 5 4 2 1 1 4 1 2 4 ...
## $ angular_lh_sig_quintile: int 3 5 2 3 2 1 2 1 4 4 ...
## $ CCN_lh_quintile : int 4 3 3 2 3 1 5 1 4 3 ...
## $ DAN_lh_quintile : int 4 5 4 2 1 1 4 1 3 4 ...
## $ hipp_tail_quintile : int 3 5 3 2 2 1 4 1 3 5 ...
## $ vtc_quintile : int 3 5 4 3 1 2 2 1 5 4 ...
## $ hipp_logit_quintile : int 2 5 3 2 4 2 5 3 1 3 ...
## $ ang_quintile : int 2 5 4 1 1 1 4 2 4 5 ...# set up contrasts
contrasts(dm$pcorr) = c(-1,1); contrasts(dm$pcorr)## [,1]
## 0 -1
## 1 1contrasts(dm$reps) = c(-1,1); contrasts(dm$reps)## [,1]
## 2 -1
## 4 1contrasts(dm$group) = c(1,-1); contrasts(dm$group)## [,1]
## control 1
## stress -1contrasts(dm$shockCond) = c(1,-1); contrasts(dm$shockCond)## [,1]
## safe 1
## threat -1Trial-wise analysis counts/group:
dm %>% group_by(subid, group) %>% summarise(n()) %>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 22
## 2 stress 20dm %>% mutate(avg = (X4+X6+X8+X10)/4) %>%
filter(round(vtc_logit,3) != round(avg,3)) %>%
dplyr::select(vtc_logit, avg)## # A tibble: 0 x 2
## # ... with 2 variables: vtc_logit <dbl>, avg <dbl>Trial counts/cond
# all subjs must have at least 6 trials of each type for sh vs cr analyses
dt %>% filter(mem_conditions %in% c('sourcehit', 'CR'))%>%
mutate(mem_conditions = factor(mem_conditions)) %>%
group_by(subid, mem_conditions) %>% summarise(n()) %>%
ungroup() %>%
complete(nesting(subid), mem_conditions, fill = list(`n()` = 0)) %>%
filter(`n()` < 6)## # A tibble: 2 x 3
## subid mem_conditions `n()`
## <fctr> <fctr> <dbl>
## 1 ap151 sourcehit 3
## 2 ap156 sourcehit 5# must exclude: ap151, ap156
# need at least 2 runs with BOTH 1 SH and 1 CR
dt %>% filter(mem_conditions %in% c('sourcehit', 'CR'))%>%
mutate(mem_conditions = factor(mem_conditions)) %>%
group_by(subid, run, mem_conditions) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, run), mem_conditions, fill = list(count = 0)) %>%
spread(key=mem_conditions, value=count, fill = 0) %>%
filter(CR == 0 | sourcehit == 0)## # A tibble: 17 x 4
## subid run CR sourcehit
## <fctr> <int> <dbl> <dbl>
## 1 ap110 6 12 0
## 2 ap121 4 0 10
## 3 ap121 5 0 9
## 4 ap121 6 0 5
## 5 ap151 2 8 0
## 6 ap151 3 6 0
## 7 ap151 5 4 0
## 8 ap151 6 5 0
## 9 ap156 2 6 0
## 10 ap156 4 8 0
## 11 ap156 6 1 0
## 12 ap158 6 9 0
## 13 ap164 5 0 15
## 14 ap172 3 4 0
## 15 ap173 4 8 0
## 16 ap173 5 6 0
## 17 ap173 6 12 0# remaining subjs have a min of 3 runs with both
# this leaves this many subjs/group for analyses collapsing across shockCond:
dt %>% filter(!subid %in% c('ap151', 'ap156')) %>%
group_by(subid, group) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control-fmri 23
## 2 stress-fmri 22# 22 controls/22 stress
# minus 1 control for too much movement! (T1 is even blurry...)
# Now, for shockCond analyses:
# all subjs must have at least 6 trials of each type
dt %>% filter(mem_conditions %in% c('sourcehit', 'CR'))%>%
mutate(mem_conditions = factor(mem_conditions),
shockCond = factor(shockCond)) %>%
group_by(subid, group, mem_conditions, shockCond) %>% summarise(n()) %>%
ungroup() %>%
complete(nesting(subid, group), mem_conditions, shockCond, fill = list(`n()` = 0)) %>%
filter(`n()` < 6)## # A tibble: 8 x 5
## subid group mem_conditions shockCond `n()`
## <fctr> <fctr> <fctr> <fctr> <dbl>
## 1 ap121 control-fmri CR threat 1
## 2 ap151 stress-fmri sourcehit safe 2
## 3 ap151 stress-fmri sourcehit threat 1
## 4 ap156 stress-fmri sourcehit safe 0
## 5 ap156 stress-fmri sourcehit threat 5
## 6 ap158 control-fmri sourcehit safe 5
## 7 ap164 stress-fmri CR safe 5
## 8 ap173 stress-fmri sourcehit threat 1# ap121 (control): 1 threat CR
# ap158 (control): 5 safe SH
# ap164 (stress) 5 safe CR
# ap173 (stress) 1 threat SH
# 6 runs for everyone by ap155 (had to leave early on day1)
dt %>% group_by(subid, run) %>% summarise(n()) %>% group_by(subid) %>% summarise(n())## # A tibble: 47 x 2
## subid `n()`
## <fctr> <int>
## 1 ap100 6
## 2 ap101 6
## 3 ap102 6
## 4 ap103 6
## 5 ap104 6
## 6 ap105 6
## 7 ap106 6
## 8 ap107 6
## 9 ap108 6
## 10 ap109 6
## # ... with 37 more rows(47 - 6) * 6 * 2 - 2 #490 rows## [1] 490# need at least 2 runs with BOTH 1 SH and 1 CR
dt %>% filter(!subid %in% c('ap151', 'ap156', 'ap121', 'ap158', 'ap164', 'ap173')) %>%
filter(mem_conditions %in% c('sourcehit', 'CR'))%>%
mutate(mem_conditions = factor(mem_conditions)) %>%
group_by(subid, group, run, mem_conditions, shockCond) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group, run, shockCond), mem_conditions, fill = list(count = 0)) %>%
filter(count == 0)## # A tibble: 2 x 6
## subid group run shockCond mem_conditions count
## <fctr> <fctr> <int> <fctr> <fctr> <dbl>
## 1 ap110 control-fmri 6 safe sourcehit 0
## 2 ap172 stress-fmri 3 threat sourcehit 0#2 subjs are missing 1 cond for 1 run; that means they have 2 good runs (at least 1 trial/condition) for each shockC type
# dt %>% filter(subid %in% c('ap110', 'ap172')) %>%
# filter(mem_conditions %in% c('sourcehit', 'CR'))%>%
# mutate(mem_conditions = factor(mem_conditions)) %>%
# group_by(subid, group, run, mem_conditions, shockCond) %>%
# summarise(count = n()) %>%
# ungroup() %>%
# complete(nesting(subid, group, run, shockCond), mem_conditions, fill = list(count = 0))Are hipp quintiles matched across groups?
No significant differences in hipp BOLD between groups at each quintile:
dm %>% filter(hipp_quintile == 4) %>% group_by(group, subid) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 22
## 2 stress 20summary(lmer(hipp_sig ~ group + (1|subid), data=dm %>% filter(hipp_quintile == 1)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: hipp_sig ~ group + (1 | subid)
## Data: dm %>% filter(hipp_quintile == 1)
##
## REML criterion at convergence: -634.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.4097 -0.3535 0.2376 0.6225 1.8068
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.01390 0.1179
## Residual 0.03333 0.1826
## Number of obs: 1343, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.361779 0.018886 39.880000 -19.156 <2e-16 ***
## group1 -0.005891 0.018886 39.880000 -0.312 0.757
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## group1 -0.049summary(lmer(hipp_sig ~ group + (1|subid), data=dm %>% filter(hipp_quintile == 2)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: hipp_sig ~ group + (1 | subid)
## Data: dm %>% filter(hipp_quintile == 2)
##
## REML criterion at convergence: -3960.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.10945 -0.67267 0.04051 0.70505 2.51204
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.001973 0.04441
## Residual 0.002610 0.05109
## Number of obs: 1321, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.099264 0.007005 39.810000 -14.171 <2e-16 ***
## group1 0.002773 0.007005 39.810000 0.396 0.694
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## group1 -0.049summary(lmer(hipp_sig ~ group + (1|subid), data=dm %>% filter(hipp_quintile == 3)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: hipp_sig ~ group + (1 | subid)
## Data: dm %>% filter(hipp_quintile == 3)
##
## REML criterion at convergence: -4646.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1710 -0.7195 0.0329 0.7330 2.8131
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.001212 0.03481
## Residual 0.001571 0.03964
## Number of obs: 1326, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.385e-02 5.488e-03 3.990e+01 7.991 8.27e-10 ***
## group1 -1.282e-05 5.488e-03 3.990e+01 -0.002 0.998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## group1 -0.049summary(lmer(hipp_sig ~ group + (1|subid), data=dm %>% filter(hipp_quintile == 4)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: hipp_sig ~ group + (1 | subid)
## Data: dm %>% filter(hipp_quintile == 4)
##
## REML criterion at convergence: -4146.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.70952 -0.72393 -0.03588 0.69258 3.13217
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.003880 0.06229
## Residual 0.002211 0.04702
## Number of obs: 1321, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.179235 0.009709 40.000000 18.460 <2e-16 ***
## group1 -0.003666 0.009709 40.000000 -0.378 0.708
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## group1 -0.048summary(lmer(hipp_sig ~ group + (1|subid), data=dm %>% filter(hipp_quintile == 5)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: hipp_sig ~ group + (1 | subid)
## Data: dm %>% filter(hipp_quintile == 5)
##
## REML criterion at convergence: -1285.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9834 -0.6897 -0.2188 0.4620 5.6170
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.01304 0.1142
## Residual 0.01976 0.1406
## Number of obs: 1312, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.411686 0.018064 39.980000 22.790 <2e-16 ***
## group1 -0.005993 0.018064 39.980000 -0.332 0.742
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## group1 -0.049Are VTC quintiles matched across groups?
vtc logit is greater for controls at quintiles 3 and 4
head(dm)## # A tibble: 6 x 83
## X.x run trial onset duration shockCond shockTrial target
## <int> <dbl> <int> <dbl> <dbl> <fctr> <int> <fctr>
## 1 0 1 1 0.0191 10.6661 safe 0 BANDIT
## 2 2 1 3 19.8642 9.4224 safe 0 VIOLIN
## 3 3 1 4 29.3132 11.3389 safe 0 MEDAL
## 4 4 1 5 40.6786 9.7644 safe 0 MANURE
## 5 5 1 6 50.4701 9.1180 safe 0 SARDINE
## 6 7 1 8 69.7414 9.9338 safe 0 TOBACCO
## # ... with 75 more variables: associate <fctr>, resp <fctr>, acc <fctr>,
## # accSpec <fctr>, respRT <dbl>, subid <fctr>, remove <lgl>,
## # anxious <int>, happy <int>, safe <int>, stressed <int>,
## # life_stress <int>, sleep <dbl>, cond_orig <fctr>,
## # shock_and_post <int>, conf <fctr>, onset_adj <dbl>,
## # mem_conditions <fctr>, obj_conditions <fctr>, onset_code <fctr>,
## # rt_z <dbl>, mem_conditions_byrep <fctr>, onset_code_byrep <fctr>,
## # img_type <fctr>, X.y <int>, index <int>, group <fctr>,
## # condition <fctr>, category <fctr>, X0 <dbl>, X2 <dbl>, X4 <dbl>,
## # X6 <dbl>, X8 <dbl>, X10 <dbl>, X12 <dbl>, avg_logit <dbl>,
## # cond <fctr>, reps <fctr>, pcorr <fctr>, vtc_logit <dbl>,
## # imgType <fctr>, hipp_sig <dbl>, hipp_tail_sig <dbl>, DAN_sig <dbl>,
## # CCN_sig <dbl>, rsp_sig <dbl>, angular_lh_sig <dbl>, DAN_lh_sig <dbl>,
## # CCN_lh_sig <dbl>, ang_logit <dbl>, hipp_logit <dbl>,
## # vtc_nophc_logit <dbl>, ang_logit_z <dbl>, vtc_logit_z <dbl>,
## # hipp_logit_z <dbl>, CCN_sig_z <dbl>, rsp_sig_z <dbl>, DAN_sig_z <dbl>,
## # angular_lh_sig_z <dbl>, CCN_lh_sig_z <dbl>, DAN_lh_sig_z <dbl>,
## # hipp_sig_z <dbl>, hipp_tail_sig_z <dbl>, hipp_quintile <int>,
## # rsp_quintile <int>, CCN_quintile <int>, DAN_quintile <int>,
## # angular_lh_sig_quintile <int>, CCN_lh_quintile <int>,
## # DAN_lh_quintile <int>, hipp_tail_quintile <int>, vtc_quintile <int>,
## # hipp_logit_quintile <int>, ang_quintile <int>summary(lmer(vtc_logit ~ group + (1|subid), data=dm %>% filter(vtc_quintile == 1)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: vtc_logit ~ group + (1 | subid)
## Data: dm %>% filter(vtc_quintile == 1)
##
## REML criterion at convergence: 4685.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2234 -0.4557 0.2496 0.7076 1.6110
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.4035 0.6352
## Residual 1.7907 1.3382
## Number of obs: 1343, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.5467 0.1047 39.9600 -33.860 <2e-16 ***
## group1 0.1683 0.1047 39.9600 1.607 0.116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## group1 -0.051summary(lmer(vtc_logit ~ group + (1|subid), data=dm %>% filter(vtc_quintile == 2)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: vtc_logit ~ group + (1 | subid)
## Data: dm %>% filter(vtc_quintile == 2)
##
## REML criterion at convergence: 1992.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.89570 -0.82235 0.01603 0.80407 2.38116
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.2003 0.4475
## Residual 0.2372 0.4871
## Number of obs: 1321, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.16254 0.07042 39.95000 -16.508 <2e-16 ***
## group1 0.11115 0.07042 39.95000 1.578 0.122
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## group1 -0.049summary(lmer(vtc_logit ~ group + (1|subid), data=dm %>% filter(vtc_quintile == 3)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: vtc_logit ~ group + (1 | subid)
## Data: dm %>% filter(vtc_quintile == 3)
##
## REML criterion at convergence: 1418.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.25324 -0.77162 0.00782 0.78348 2.76927
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.1678 0.4097
## Residual 0.1517 0.3895
## Number of obs: 1326, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.23431 0.06419 40.02000 3.650 0.00075 ***
## group1 0.13957 0.06419 40.02000 2.174 0.03565 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## group1 -0.048summary(lmer(vtc_logit ~ group + (1|subid), data=dm %>% filter(vtc_quintile == 4)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: vtc_logit ~ group + (1 | subid)
## Data: dm %>% filter(vtc_quintile == 4)
##
## REML criterion at convergence: 2170.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.56360 -0.74246 -0.07781 0.74215 2.91486
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.2388 0.4887
## Residual 0.2711 0.5207
## Number of obs: 1321, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.67129 0.07685 40.04000 21.748 <2e-16 ***
## group1 0.17842 0.07685 40.04000 2.322 0.0254 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## group1 -0.049summary(lmer(vtc_logit ~ group + (1|subid), data=dm %>% filter(vtc_quintile == 5)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: vtc_logit ~ group + (1 | subid)
## Data: dm %>% filter(vtc_quintile == 5)
##
## REML criterion at convergence: 4829.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5571 -0.7213 -0.2643 0.4988 4.9278
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.6685 0.8176
## Residual 2.1498 1.4662
## Number of obs: 1312, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.2302 0.1327 40.0600 31.884 <2e-16 ***
## group1 0.1980 0.1327 40.0600 1.492 0.143
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## group1 -0.050VTC Mediation: Indirect effect of hipp -> VTC -> pcorr
Template borrowed from Alan Gordon (https://github.com/amgordon/Gordon_CerCor_2014)
Overall effect of hipp -> pcorr
mean(dm$hipp_sig_z)## [1] -3.41811e-18sd(dm$hipp_sig_z)## [1] 0.9968995mean(dm$vtc_logit_z)## [1] 5.855437e-19sd(dm$vtc_logit_z)## [1] 0.9968995# standardize continuous vars
dCleanStandardized = dm
dCleanStandardized$rGenHipp<-scale(dCleanStandardized$hipp_sig_z)
dCleanStandardized$ERActUnsigned<-scale(dCleanStandardized$vtc_logit_z)
dCleanStandardized$ERActUnsigned_infpar<-scale(dCleanStandardized$ang_logit_z)
dCleanStandardized$rGenANG<-scale(dCleanStandardized$angular_lh_sig_z)
dCleanStandardized$rGenRSP<-scale(dCleanStandardized$rsp_sig_z)
# overall effect
deq.0<-glmer(pcorr ~ group*(rGenHipp) + reps + shockCond +
(-1 + rGenHipp|subid) +
(1|subid),
data=dCleanStandardized, family = "binomial")
# coef(deq.0)
summary(deq.0)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: pcorr ~ group * (rGenHipp) + reps + shockCond + (-1 + rGenHipp |
## subid) + (1 | subid)
## Data: dCleanStandardized
##
## AIC BIC logLik deviance df.resid
## 7514.9 7569.3 -3749.5 7498.9 6615
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0622 -0.7018 -0.3698 0.7778 5.2359
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.07214 0.2686
## subid.1 (Intercept) 1.00406 1.0020
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.42992 0.15771 -2.726 0.00641 **
## group1 0.44368 0.15772 2.813 0.00491 **
## rGenHipp 0.33467 0.05136 6.516 7.22e-11 ***
## reps1 0.39755 0.02880 13.803 < 2e-16 ***
## shockCond1 0.01950 0.02845 0.685 0.49305
## group1:rGenHipp 0.09507 0.05134 1.852 0.06406 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp reps1 shckC1
## group1 -0.048
## rGenHipp -0.004 0.007
## reps1 -0.013 0.014 0.002
## shockCond1 -0.004 0.004 0.003 0.008
## grp1:rGnHpp 0.006 -0.003 -0.048 -0.009 -0.005fixef(deq.0)## (Intercept) group1 rGenHipp reps1
## -0.42992200 0.44368074 0.33466534 0.39754889
## shockCond1 group1:rGenHipp
## 0.01950378 0.09506868fixef(deq.0)[3] # overall effect of hipp## rGenHipp
## 0.3346653fixef(deq.0)[6] # overall effect of hipp:group## group1:rGenHipp
## 0.09506868# look at interaction of hipp * group
summary(glmer(pcorr ~ (rGenHipp) + reps + shockCond +
(-1 + rGenHipp|subid) +
(1|subid),
data=dCleanStandardized %>% filter(group == 'control'),
family = "binomial"))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## pcorr ~ (rGenHipp) + reps + shockCond + (-1 + rGenHipp | subid) +
## (1 | subid)
## Data: dCleanStandardized %>% filter(group == "control")
##
## AIC BIC logLik deviance df.resid
## 3969.8 4006.8 -1978.9 3957.8 3547
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2453 -0.6941 0.2142 0.7092 4.4509
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.08692 0.2948
## subid.1 (Intercept) 1.42038 1.1918
## Number of obs: 3553, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.01359 0.25749 0.053 0.958
## rGenHipp 0.43188 0.07570 5.705 1.16e-08 ***
## reps1 0.43333 0.03975 10.901 < 2e-16 ***
## shockCond1 0.02095 0.03926 0.534 0.594
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rGnHpp reps1
## rGenHipp 0.003
## reps1 0.001 -0.004
## shockCond1 0.000 -0.001 0.005summary(glmer(pcorr ~ (rGenHipp) + reps + shockCond +
(-1 + rGenHipp|subid) +
(1|subid),
data=dCleanStandardized %>% filter(group == 'stress'),
family = "binomial"))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## pcorr ~ (rGenHipp) + reps + shockCond + (-1 + rGenHipp | subid) +
## (1 | subid)
## Data: dCleanStandardized %>% filter(group == "stress")
##
## AIC BIC logLik deviance df.resid
## 3547.3 3583.5 -1767.7 3535.3 3064
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1307 -0.6966 -0.4657 0.9263 4.7099
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.05503 0.2346
## subid.1 (Intercept) 0.55524 0.7451
## Number of obs: 3070, groups: subid, 20
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.85812 0.17235 -4.979 6.39e-07 ***
## rGenHipp 0.23557 0.06801 3.464 0.000533 ***
## reps1 0.35696 0.04179 8.542 < 2e-16 ***
## shockCond1 0.01766 0.04132 0.427 0.669089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rGnHpp reps1
## rGenHipp -0.014
## reps1 -0.033 0.010
## shockCond1 -0.012 0.009 0.011# polynomial interaction?
deq.0b<-glmer(pcorr ~ group*(poly(hipp_sig_z, 2)) + reps + shockCond +
(-1 + poly(hipp_sig_z, 2)|subid) +
(1|subid),
data=dCleanStandardized, family = "binomial")
coef(deq.0b)## $subid
## (Intercept) group1 poly(hipp_sig_z, 2)1 poly(hipp_sig_z, 2)2
## ap100 -0.179719928 0.4489898 18.955811 0.67998499
## ap101 0.122599966 0.4489898 52.908495 7.39666695
## ap102 -0.801141710 0.4489898 18.981810 0.68512834
## ap103 0.481051461 0.4489898 59.173250 8.63599066
## ap104 1.620984480 0.4489898 31.504644 3.16245521
## ap105 0.592531646 0.4489898 37.827055 4.41318459
## ap107 0.782620511 0.4489898 43.473270 5.53014593
## ap108 -1.890145730 0.4489898 5.985579 -1.88584829
## ap109 -0.995578663 0.4489898 11.239920 -0.84640908
## ap110 -2.395724028 0.4489898 14.897408 -0.12286756
## ap111 -0.722131590 0.4489898 51.438805 7.10592636
## ap113 -0.645582988 0.4489898 7.258866 -1.63396029
## ap114 0.072886031 0.4489898 14.630995 -0.17557115
## ap115 -0.522147636 0.4489898 53.715034 7.55622042
## ap116 1.621008256 0.4489898 32.417512 3.34304279
## ap117 -0.382707518 0.4489898 36.592067 4.16887289
## ap118 -0.992029054 0.4489898 49.228797 6.66873197
## ap119 0.414235274 0.4489898 20.525964 0.99060026
## ap120 -0.610417981 0.4489898 37.600349 4.36833667
## ap121 -1.824911605 0.4489898 -19.022156 -6.83299754
## ap122 -0.141853246 0.4489898 12.199591 -0.65656260
## ap150 0.106077938 0.4489898 46.295246 6.08840274
## ap152 -0.643696928 0.4489898 38.203901 4.48773441
## ap153 -0.937262784 0.4489898 9.900484 -1.11138302
## ap154 -0.513365474 0.4489898 13.648573 -0.36991790
## ap155 -0.516737748 0.4489898 15.131911 -0.07647738
## ap157 -0.359096351 0.4489898 46.675166 6.16356003
## ap158 -2.970970431 0.4489898 14.406755 -0.21993103
## ap159 -0.396201275 0.4489898 27.995848 2.46832827
## ap160 -0.014873925 0.4489898 10.163190 -1.05941341
## ap161 -1.164609106 0.4489898 9.358487 -1.21860362
## ap162 -0.622031049 0.4489898 6.351879 -1.81338423
## ap163 1.155909577 0.4489898 46.360286 6.10126907
## ap164 0.476996285 0.4489898 26.667038 2.20545693
## ap165 -0.003155807 0.4489898 62.478610 9.28987303
## ap166 0.219122524 0.4489898 14.177352 -0.26531309
## ap167 -0.706533023 0.4489898 29.757268 2.81678057
## ap169 0.185149007 0.4489898 2.241981 -2.62642458
## ap170 0.242310467 0.4489898 40.032914 4.84955847
## ap171 -0.988071847 0.4489898 40.776270 4.99661219
## ap172 -1.483956181 0.4489898 32.280958 3.31602923
## ap173 -2.321621448 0.4489898 38.832794 4.61214471
## reps1 shockCond1 group1:poly(hipp_sig_z, 2)1
## ap100 0.3967185 0.02083239 8.113781
## ap101 0.3967185 0.02083239 8.113781
## ap102 0.3967185 0.02083239 8.113781
## ap103 0.3967185 0.02083239 8.113781
## ap104 0.3967185 0.02083239 8.113781
## ap105 0.3967185 0.02083239 8.113781
## ap107 0.3967185 0.02083239 8.113781
## ap108 0.3967185 0.02083239 8.113781
## ap109 0.3967185 0.02083239 8.113781
## ap110 0.3967185 0.02083239 8.113781
## ap111 0.3967185 0.02083239 8.113781
## ap113 0.3967185 0.02083239 8.113781
## ap114 0.3967185 0.02083239 8.113781
## ap115 0.3967185 0.02083239 8.113781
## ap116 0.3967185 0.02083239 8.113781
## ap117 0.3967185 0.02083239 8.113781
## ap118 0.3967185 0.02083239 8.113781
## ap119 0.3967185 0.02083239 8.113781
## ap120 0.3967185 0.02083239 8.113781
## ap121 0.3967185 0.02083239 8.113781
## ap122 0.3967185 0.02083239 8.113781
## ap150 0.3967185 0.02083239 8.113781
## ap152 0.3967185 0.02083239 8.113781
## ap153 0.3967185 0.02083239 8.113781
## ap154 0.3967185 0.02083239 8.113781
## ap155 0.3967185 0.02083239 8.113781
## ap157 0.3967185 0.02083239 8.113781
## ap158 0.3967185 0.02083239 8.113781
## ap159 0.3967185 0.02083239 8.113781
## ap160 0.3967185 0.02083239 8.113781
## ap161 0.3967185 0.02083239 8.113781
## ap162 0.3967185 0.02083239 8.113781
## ap163 0.3967185 0.02083239 8.113781
## ap164 0.3967185 0.02083239 8.113781
## ap165 0.3967185 0.02083239 8.113781
## ap166 0.3967185 0.02083239 8.113781
## ap167 0.3967185 0.02083239 8.113781
## ap169 0.3967185 0.02083239 8.113781
## ap170 0.3967185 0.02083239 8.113781
## ap171 0.3967185 0.02083239 8.113781
## ap172 0.3967185 0.02083239 8.113781
## ap173 0.3967185 0.02083239 8.113781
## group1:poly(hipp_sig_z, 2)2
## ap100 6.283574
## ap101 6.283574
## ap102 6.283574
## ap103 6.283574
## ap104 6.283574
## ap105 6.283574
## ap107 6.283574
## ap108 6.283574
## ap109 6.283574
## ap110 6.283574
## ap111 6.283574
## ap113 6.283574
## ap114 6.283574
## ap115 6.283574
## ap116 6.283574
## ap117 6.283574
## ap118 6.283574
## ap119 6.283574
## ap120 6.283574
## ap121 6.283574
## ap122 6.283574
## ap150 6.283574
## ap152 6.283574
## ap153 6.283574
## ap154 6.283574
## ap155 6.283574
## ap157 6.283574
## ap158 6.283574
## ap159 6.283574
## ap160 6.283574
## ap161 6.283574
## ap162 6.283574
## ap163 6.283574
## ap164 6.283574
## ap165 6.283574
## ap166 6.283574
## ap167 6.283574
## ap169 6.283574
## ap170 6.283574
## ap171 6.283574
## ap172 6.283574
## ap173 6.283574
##
## attr(,"class")
## [1] "coef.mer"summary(deq.0b)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: pcorr ~ group * (poly(hipp_sig_z, 2)) + reps + shockCond + (-1 +
## poly(hipp_sig_z, 2) | subid) + (1 | subid)
## Data: dCleanStandardized
##
## AIC BIC logLik deviance df.resid
## 7514.3 7595.9 -3745.1 7490.3 6611
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1524 -0.7027 -0.3714 0.7828 5.2445
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid poly(hipp_sig_z, 2)1 500.80 22.379
## poly(hipp_sig_z, 2)2 19.60 4.427 1.00
## subid.1 (Intercept) 1.01 1.005
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.42550 0.15817 -2.690 0.00714 **
## group1 0.44899 0.15817 2.839 0.00453 **
## poly(hipp_sig_z, 2)1 27.95865 4.25162 6.576 4.83e-11 ***
## poly(hipp_sig_z, 2)2 2.46097 2.65616 0.927 0.35418
## reps1 0.39672 0.02882 13.763 < 2e-16 ***
## shockCond1 0.02083 0.02849 0.731 0.46459
## group1:poly(hipp_sig_z, 2)1 8.11378 4.24576 1.911 0.05600 .
## group1:poly(hipp_sig_z, 2)2 6.28357 2.62071 2.398 0.01650 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 p(__,2)1 p(__,2)2 reps1 shckC1 g1:(__,2)1
## group1 -0.048
## ply(h__,2)1 -0.002 0.010
## ply(h__,2)2 0.012 0.003 0.203
## reps1 -0.013 0.014 0.003 0.000
## shockCond1 -0.004 0.004 0.003 0.013 0.008
## gr1:(__,2)1 0.009 -0.002 -0.052 0.086 -0.008 -0.003
## gr1:(__,2)2 0.002 0.011 0.083 -0.059 -0.009 0.011 0.204summary(glmer(pcorr ~ (poly(hipp_sig_z, 2)) + reps + shockCond +
(-1 + poly(hipp_sig_z, 2)|subid) +
(1|subid),
data=dCleanStandardized %>% filter(group == 'control'),
family = "binomial"))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## pcorr ~ (poly(hipp_sig_z, 2)) + reps + shockCond + (-1 + poly(hipp_sig_z,
## 2) | subid) + (1 | subid)
## Data: dCleanStandardized %>% filter(group == "control")
##
## AIC BIC logLik deviance df.resid
## 3968.8 4024.4 -1975.4 3950.8 3544
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3474 -0.6912 0.1892 0.7102 4.2193
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid poly(hipp_sig_z, 2)1 333.461 18.261
## poly(hipp_sig_z, 2)2 15.149 3.892 1.00
## subid.1 (Intercept) 1.431 1.196
## Number of obs: 3553, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.02471 0.25848 0.096 0.9238
## poly(hipp_sig_z, 2)1 26.28925 4.63369 5.674 1.4e-08 ***
## poly(hipp_sig_z, 2)2 6.47642 2.69315 2.405 0.0162 *
## reps1 0.43057 0.03979 10.822 < 2e-16 ***
## shockCond1 0.02282 0.03932 0.580 0.5617
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) p(__,2)1 p(__,2)2 reps1
## ply(h__,2)1 0.007
## ply(h__,2)2 0.014 0.324
## reps1 0.001 -0.004 -0.014
## shockCond1 0.000 -0.002 0.021 0.006summary(glmer(pcorr ~ (poly(hipp_sig_z, 2)) + reps + shockCond +
(-1 + poly(hipp_sig_z, 2)|subid) +
(1|subid),
data=dCleanStandardized %>% filter(group == 'stress'),
family = "binomial"))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## pcorr ~ (poly(hipp_sig_z, 2)) + reps + shockCond + (-1 + poly(hipp_sig_z,
## 2) | subid) + (1 | subid)
## Data: dCleanStandardized %>% filter(group == "stress")
##
## AIC BIC logLik deviance df.resid
## 3550.2 3604.5 -1766.1 3532.2 3061
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1385 -0.6904 -0.4658 0.9173 4.7233
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid poly(hipp_sig_z, 2)1 174.8915 13.2247
## poly(hipp_sig_z, 2)2 58.6042 7.6553 0.14
## subid.1 (Intercept) 0.5604 0.7486
## Number of obs: 3070, groups: subid, 20
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.86305 0.17317 -4.984 6.23e-07 ***
## poly(hipp_sig_z, 2)1 13.74035 3.82913 3.588 0.000333 ***
## poly(hipp_sig_z, 2)2 -3.34795 3.17916 -1.053 0.292299
## reps1 0.36092 0.04201 8.592 < 2e-16 ***
## shockCond1 0.01843 0.04144 0.445 0.656473
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) p(__,2)1 p(__,2)2 reps1
## ply(h__,2)1 -0.018
## ply(h__,2)2 0.016 -0.029
## reps1 -0.033 0.015 -0.002
## shockCond1 -0.012 0.005 0.003 0.012# exploratory check: is there an interaction between reps and hipp?
summary(glmer(pcorr ~ group*(rGenHipp) + reps + shockCond + rGenHipp:reps +
(-1 + rGenHipp|subid) +
# (-1 + rGenHipp:reps|subid) + # remove to converge
(1|subid),
data=dCleanStandardized, family = "binomial",
control=glmerControl(optimizer='Nelder_Mead')))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: pcorr ~ group * (rGenHipp) + reps + shockCond + rGenHipp:reps +
## (-1 + rGenHipp | subid) + (1 | subid)
## Data: dCleanStandardized
## Control: glmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 7516.7 7577.8 -3749.3 7498.7 6614
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0897 -0.7009 -0.3689 0.7791 5.2180
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.07266 0.2696
## subid.1 (Intercept) 1.00441 1.0022
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.43003 0.15774 -2.726 0.00641 **
## group1 0.44348 0.15775 2.811 0.00493 **
## rGenHipp 0.33363 0.05152 6.476 9.44e-11 ***
## reps1 0.39741 0.02880 13.797 < 2e-16 ***
## shockCond1 0.01922 0.02846 0.675 0.49943
## group1:rGenHipp 0.09590 0.05149 1.862 0.06253 .
## rGenHipp:reps1 0.01562 0.02973 0.525 0.59940
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp reps1 shckC1 gr1:GH
## group1 -0.048
## rGenHipp -0.003 0.007
## reps1 -0.012 0.014 0.001
## shockCond1 -0.004 0.004 0.004 0.008
## grp1:rGnHpp 0.006 -0.003 -0.049 -0.008 -0.005
## rGnHpp:rps1 -0.002 -0.002 -0.037 -0.007 -0.019 0.031Full model, controlling for VTC reinstatement
# triple check zstats are same
# summary(glmer(pcorr ~ group*(hipp_sig_z + vtc_logit_z) + reps + shockCond +
# (-1 + hipp_sig_z|subid) +
# (-1 + vtc_logit_z|subid) +
# (1|subid),
# data=dCleanStandardized, family = "binomial"))
# effect of things on subsequent memory
deq.1<-glmer(pcorr ~ group*(rGenHipp + ERActUnsigned) + reps + shockCond +
(-1 + rGenHipp|subid) +
(-1 + ERActUnsigned|subid) +
(1|subid),
data=dCleanStandardized, family = "binomial")
# uncorrelate random slopes and intercepts
summary(deq.1)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: pcorr ~ group * (rGenHipp + ERActUnsigned) + reps + shockCond +
## (-1 + rGenHipp | subid) + (-1 + ERActUnsigned | subid) +
## (1 | subid)
## Data: dCleanStandardized
##
## AIC BIC logLik deviance df.resid
## 7428.4 7503.1 -3703.2 7406.4 6612
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1434 -0.6772 -0.3542 0.7519 5.0011
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.07081 0.2661
## subid.1 ERActUnsigned 0.04681 0.2164
## subid.2 (Intercept) 1.03744 1.0186
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.44188 0.16030 -2.757 0.00584 **
## group1 0.44877 0.16030 2.800 0.00512 **
## rGenHipp 0.26223 0.05210 5.034 4.81e-07 ***
## ERActUnsigned 0.26422 0.04601 5.743 9.32e-09 ***
## reps1 0.38684 0.02916 13.264 < 2e-16 ***
## shockCond1 0.02031 0.02875 0.707 0.47984
## group1:rGenHipp 0.12063 0.05208 2.316 0.02055 *
## group1:ERActUnsigned -0.03333 0.04600 -0.725 0.46872
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp ERActU reps1 shckC1 gr1:GH
## group1 -0.049
## rGenHipp -0.002 0.005
## ERActUnsgnd -0.008 0.005 -0.110
## reps1 -0.013 0.013 0.007 -0.019
## shockCond1 -0.004 0.005 0.003 0.003 0.010
## grp1:rGnHpp 0.004 -0.001 -0.061 0.036 -0.010 -0.006
## grp1:ERActU 0.005 -0.008 0.036 -0.071 0.005 0.001 -0.110fixef(deq.1)## (Intercept) group1 rGenHipp
## -0.44187904 0.44876921 0.26222980
## ERActUnsigned reps1 shockCond1
## 0.26422122 0.38683695 0.02031445
## group1:rGenHipp group1:ERActUnsigned
## 0.12062548 -0.03332887fixef(deq.1)[4] # subs ~ VTC direct effect ## ERActUnsigned
## 0.2642212fixef(deq.1)[3] # subs ~ hipp direct effect ## rGenHipp
## 0.2622298fixef(deq.1)[7] # hipp:group interaction on pcorr direct## group1:rGenHipp
## 0.1206255summary(deq.1)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: pcorr ~ group * (rGenHipp + ERActUnsigned) + reps + shockCond +
## (-1 + rGenHipp | subid) + (-1 + ERActUnsigned | subid) +
## (1 | subid)
## Data: dCleanStandardized
##
## AIC BIC logLik deviance df.resid
## 7428.4 7503.1 -3703.2 7406.4 6612
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1434 -0.6772 -0.3542 0.7519 5.0011
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.07081 0.2661
## subid.1 ERActUnsigned 0.04681 0.2164
## subid.2 (Intercept) 1.03744 1.0186
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.44188 0.16030 -2.757 0.00584 **
## group1 0.44877 0.16030 2.800 0.00512 **
## rGenHipp 0.26223 0.05210 5.034 4.81e-07 ***
## ERActUnsigned 0.26422 0.04601 5.743 9.32e-09 ***
## reps1 0.38684 0.02916 13.264 < 2e-16 ***
## shockCond1 0.02031 0.02875 0.707 0.47984
## group1:rGenHipp 0.12063 0.05208 2.316 0.02055 *
## group1:ERActUnsigned -0.03333 0.04600 -0.725 0.46872
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp ERActU reps1 shckC1 gr1:GH
## group1 -0.049
## rGenHipp -0.002 0.005
## ERActUnsgnd -0.008 0.005 -0.110
## reps1 -0.013 0.013 0.007 -0.019
## shockCond1 -0.004 0.005 0.003 0.003 0.010
## grp1:rGnHpp 0.004 -0.001 -0.061 0.036 -0.010 -0.006
## grp1:ERActU 0.005 -0.008 0.036 -0.071 0.005 0.001 -0.110# quick: what about "b" -- effect of VTC on pcorr
summary(glmer(pcorr ~ group*(ERActUnsigned) + reps + shockCond +
(-1 + ERActUnsigned|subid) +
(1|subid),
data=dCleanStandardized, family = "binomial"))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## pcorr ~ group * (ERActUnsigned) + reps + shockCond + (-1 + ERActUnsigned |
## subid) + (1 | subid)
## Data: dCleanStandardized
##
## AIC BIC logLik deviance df.resid
## 7550.8 7605.1 -3767.4 7534.8 6615
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8002 -0.6986 -0.3596 0.7835 5.6495
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid ERActUnsigned 0.04786 0.2188
## subid.1 (Intercept) 0.99038 0.9952
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.441580 0.156672 -2.818 0.00482 **
## group1 0.436634 0.156660 2.787 0.00532 **
## ERActUnsigned 0.326077 0.045059 7.237 4.6e-13 ***
## reps1 0.389862 0.028704 13.582 < 2e-16 ***
## shockCond1 0.020818 0.028332 0.735 0.46246
## group1:ERActUnsigned -0.006766 0.045038 -0.150 0.88059
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 ERActU reps1 shckC1
## group1 -0.049
## ERActUnsgnd -0.009 0.006
## reps1 -0.013 0.013 -0.017
## shockCond1 -0.004 0.005 0.004 0.008
## grp1:ERActU 0.006 -0.009 -0.057 0.003 -0.002# quadratic effect?
summary(glmer(pcorr ~ group*(poly(vtc_logit_z, 2)) + reps + shockCond +
(-1 + poly(vtc_logit_z, 2)|subid) +
(1|subid),
data=dCleanStandardized, family = "binomial",
control=glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5))))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## pcorr ~ group * (poly(vtc_logit_z, 2)) + reps + shockCond + (-1 +
## poly(vtc_logit_z, 2) | subid) + (1 | subid)
## Data: dCleanStandardized
## Control:
## glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## AIC BIC logLik deviance df.resid
## 7546.3 7627.9 -3761.1 7522.3 6611
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6628 -0.7041 -0.3541 0.7701 5.7804
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid poly(vtc_logit_z, 2)1 376.4429 19.4021
## poly(vtc_logit_z, 2)2 83.4432 9.1347 -0.12
## subid.1 (Intercept) 0.9994 0.9997
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.44809 0.15740 -2.847 0.00442 **
## group1 0.43822 0.15737 2.785 0.00536 **
## poly(vtc_logit_z, 2)1 27.67339 3.80521 7.273 3.53e-13 ***
## poly(vtc_logit_z, 2)2 -8.04984 2.87238 -2.803 0.00507 **
## reps1 0.39246 0.02881 13.622 < 2e-16 ***
## shockCond1 0.02150 0.02843 0.756 0.44944
## group1:poly(vtc_logit_z, 2)1 -0.85992 3.77090 -0.228 0.81961
## group1:poly(vtc_logit_z, 2)2 -1.51218 2.83373 -0.534 0.59360
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 p(__,2)1 p(__,2)2 reps1 shckC1 g1:(__,2)1
## group1 -0.049
## ply(v__,2)1 -0.013 0.007
## ply(v__,2)2 0.012 -0.004 -0.123
## reps1 -0.013 0.014 -0.013 -0.021
## shockCond1 -0.004 0.005 0.005 -0.006 0.008
## gr1:(__,2)1 0.007 -0.011 -0.054 0.024 0.000 -0.002
## gr1:(__,2)2 -0.004 0.010 0.024 -0.041 0.016 0.022 -0.106# interrogate interaction
summary(glmer(pcorr ~ (rGenHipp + ERActUnsigned) + reps + shockCond +
(-1 + rGenHipp|subid) +
(-1 + ERActUnsigned|subid) +
(1|subid),
data=dCleanStandardized %>% filter(group == 'control'),
family = "binomial"))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: pcorr ~ (rGenHipp + ERActUnsigned) + reps + shockCond + (-1 +
## rGenHipp | subid) + (-1 + ERActUnsigned | subid) + (1 | subid)
## Data: dCleanStandardized %>% filter(group == "control")
##
## AIC BIC logLik deviance df.resid
## 3927.6 3977.0 -1955.8 3911.6 3545
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3056 -0.6773 0.2169 0.6892 4.2819
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.08969 0.2995
## subid.1 ERActUnsigned 0.06196 0.2489
## subid.2 (Intercept) 1.46419 1.2100
## Number of obs: 3553, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.005957 0.261413 0.023 0.981821
## rGenHipp 0.385538 0.077465 4.977 6.46e-07 ***
## ERActUnsigned 0.231443 0.068316 3.388 0.000704 ***
## reps1 0.428169 0.040285 10.628 < 2e-16 ***
## shockCond1 0.023858 0.039666 0.601 0.547522
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rGnHpp ERActU reps1
## rGenHipp 0.003
## ERActUnsgnd -0.004 -0.069
## reps1 0.001 0.000 -0.011
## shockCond1 0.000 -0.002 0.004 0.007summary(glmer(pcorr ~ (rGenHipp + ERActUnsigned) + reps + shockCond +
(-1 + rGenHipp|subid) +
(-1 + ERActUnsigned|subid) +
(1|subid),
data=dCleanStandardized %>% filter(group == 'stress'),
family = "binomial"))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: pcorr ~ (rGenHipp + ERActUnsigned) + reps + shockCond + (-1 +
## rGenHipp | subid) + (-1 + ERActUnsigned | subid) + (1 | subid)
## Data: dCleanStandardized %>% filter(group == "stress")
##
## AIC BIC logLik deviance df.resid
## 3503.9 3552.1 -1743.9 3487.9 3062
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4914 -0.6757 -0.4465 0.8937 4.5946
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.05127 0.2264
## subid.1 ERActUnsigned 0.03277 0.1810
## subid.2 (Intercept) 0.58093 0.7622
## Number of obs: 3070, groups: subid, 20
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.87331 0.17621 -4.956 7.19e-07 ***
## rGenHipp 0.13802 0.06866 2.010 0.0444 *
## ERActUnsigned 0.29547 0.06146 4.808 1.53e-06 ***
## reps1 0.34025 0.04231 8.042 8.82e-16 ***
## shockCond1 0.01644 0.04176 0.394 0.6939
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rGnHpp ERActU reps1
## rGenHipp -0.008
## ERActUnsgnd -0.017 -0.160
## reps1 -0.031 0.018 -0.029
## shockCond1 -0.011 0.010 0.001 0.014# poly?
# both linear and quadratic interactions
summary(glmer(pcorr ~ group*(poly(hipp_sig_z, 2) + ERActUnsigned) +
reps + shockCond +
(-1 + poly(hipp_sig_z, 2)|subid) +
(-1 + ERActUnsigned|subid) +
(1|subid),
data=dCleanStandardized, family = "binomial",
control=glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5))))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: pcorr ~ group * (poly(hipp_sig_z, 2) + ERActUnsigned) + reps +
## shockCond + (-1 + poly(hipp_sig_z, 2) | subid) + (-1 + ERActUnsigned |
## subid) + (1 | subid)
## Data: dCleanStandardized
## Control:
## glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## AIC BIC logLik deviance df.resid
## 7428.1 7530.1 -3699.1 7398.1 6608
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2373 -0.6744 -0.3533 0.7554 4.9623
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid poly(hipp_sig_z, 2)1 493.62983 22.2178
## poly(hipp_sig_z, 2)2 23.08107 4.8043 0.48
## subid.1 ERActUnsigned 0.04661 0.2159
## subid.2 (Intercept) 1.04345 1.0215
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.43867 0.16078 -2.728 0.00636 **
## group1 0.45414 0.16077 2.825 0.00473 **
## poly(hipp_sig_z, 2)1 22.17829 4.20261 5.277 1.31e-07 ***
## poly(hipp_sig_z, 2)2 2.33975 2.70260 0.866 0.38663
## ERActUnsigned 0.26556 0.04602 5.771 7.89e-09 ***
## reps1 0.38623 0.02921 13.221 < 2e-16 ***
## shockCond1 0.02184 0.02879 0.758 0.44823
## group1:poly(hipp_sig_z, 2)1 9.96197 4.20164 2.371 0.01774 *
## group1:poly(hipp_sig_z, 2)2 6.74722 2.64882 2.547 0.01086 *
## group1:ERActUnsigned -0.03230 0.04596 -0.703 0.48219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 p(__,2)1 p(__,2)2 ERActU reps1 shckC1
## group1 -0.049
## ply(h__,2)1 -0.001 0.007
## ply(h__,2)2 0.013 0.002 0.083
## ERActUnsgnd -0.009 0.005 -0.102 0.003
## reps1 -0.013 0.013 0.009 -0.005 -0.017
## shockCond1 -0.004 0.005 0.003 0.014 0.004 0.010
## gr1:(__,2)1 0.007 0.000 -0.065 0.078 0.031 -0.010 -0.005
## gr1:(__,2)2 0.001 0.011 0.067 -0.048 0.013 -0.006 0.012
## grp1:ERActU 0.005 -0.008 0.033 0.010 -0.070 0.005 0.001
## g1:(__,2)1 g1:(__,2)2
## group1
## ply(h__,2)1
## ply(h__,2)2
## ERActUnsgnd
## reps1
## shockCond1
## gr1:(__,2)1
## gr1:(__,2)2 0.093
## grp1:ERActU -0.105 0.008# Fixed effects:
# Estimate Std. Error z value Pr(>|z|)
# (Intercept) -0.43867 0.16078 -2.728 0.00636 **
# group1 0.45414 0.16077 2.825 0.00473 **
# poly(hipp_sig_z, 2)1 22.17829 4.20261 5.277 1.31e-07 ***
# poly(hipp_sig_z, 2)2 2.33975 2.70260 0.866 0.38663
# ERActUnsigned 0.26556 0.04602 5.771 7.89e-09 ***
# reps1 0.38623 0.02921 13.221 < 2e-16 ***
# shockCond1 0.02184 0.02879 0.758 0.44823
# group1:poly(hipp_sig_z, 2)1 9.96197 4.20164 2.371 0.01774 *
# group1:poly(hipp_sig_z, 2)2 6.74722 2.64882 2.547 0.01086 *
# group1:ERActUnsigned -0.03230 0.04596 -0.703 0.48219
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# holds w/simple effects?
#controls:
contrasts(dCleanStandardized$group) = c(0,1); contrasts(dCleanStandardized$group)## [,1]
## control 0
## stress 1summary(glmer(pcorr ~ group*(rGenHipp + ERActUnsigned) + reps + shockCond +
(-1 + rGenHipp|subid) +
(-1 + ERActUnsigned|subid) +
(1|subid),
data=dCleanStandardized, family = "binomial",
control=glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5))))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: pcorr ~ group * (rGenHipp + ERActUnsigned) + reps + shockCond +
## (-1 + rGenHipp | subid) + (-1 + ERActUnsigned | subid) +
## (1 | subid)
## Data: dCleanStandardized
## Control:
## glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## AIC BIC logLik deviance df.resid
## 7428.4 7503.1 -3703.2 7406.4 6612
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1434 -0.6772 -0.3542 0.7519 5.0012
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.07081 0.2661
## subid.1 ERActUnsigned 0.04681 0.2163
## subid.2 (Intercept) 1.03745 1.0186
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.006883 0.221105 0.031 0.975164
## group1 -0.897553 0.320598 -2.800 0.005116 **
## rGenHipp 0.382864 0.071379 5.364 8.15e-08 ***
## ERActUnsigned 0.230903 0.062699 3.683 0.000231 ***
## reps1 0.386842 0.029164 13.264 < 2e-16 ***
## shockCond1 0.020312 0.028751 0.706 0.479893
## group1:rGenHipp -0.241264 0.104165 -2.316 0.020549 *
## group1:ERActUnsigned 0.066640 0.091993 0.724 0.468819
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp ERActU reps1 shckC1 gr1:GH
## group1 -0.690
## rGenHipp 0.003 -0.002
## ERActUnsgnd -0.004 0.003 -0.079
## reps1 0.001 -0.013 -0.002 -0.010
## shockCond1 0.000 -0.005 -0.002 0.003 0.010
## grp1:rGnHpp -0.002 -0.001 -0.685 0.054 0.010 0.006
## grp1:ERActU 0.003 -0.008 0.054 -0.681 -0.005 -0.001 -0.110#stress:
contrasts(dCleanStandardized$group) = c(1,0); contrasts(dCleanStandardized$group)## [,1]
## control 1
## stress 0summary(glmer(pcorr ~ group*(rGenHipp + ERActUnsigned) + reps + shockCond +
(-1 + rGenHipp|subid) +
(-1 + ERActUnsigned|subid) +
(1|subid),
data=dCleanStandardized, family = "binomial",
control=glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5))))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: pcorr ~ group * (rGenHipp + ERActUnsigned) + reps + shockCond +
## (-1 + rGenHipp | subid) + (-1 + ERActUnsigned | subid) +
## (1 | subid)
## Data: dCleanStandardized
## Control:
## glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## AIC BIC logLik deviance df.resid
## 7428.4 7503.1 -3703.2 7406.4 6612
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1434 -0.6772 -0.3542 0.7519 5.0012
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.07081 0.2661
## subid.1 ERActUnsigned 0.04681 0.2163
## subid.2 (Intercept) 1.03745 1.0186
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.89067 0.23215 -3.837 0.000125 ***
## group1 0.89755 0.32059 2.800 0.005115 **
## rGenHipp 0.14160 0.07588 1.866 0.062033 .
## ERActUnsigned 0.29754 0.06733 4.419 9.92e-06 ***
## reps1 0.38684 0.02916 13.264 < 2e-16 ***
## shockCond1 0.02031 0.02875 0.706 0.479892
## group1:rGenHipp 0.24126 0.10416 2.316 0.020547 *
## group1:ERActUnsigned -0.06664 0.09199 -0.724 0.468809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp ERActU reps1 shckC1 gr1:GH
## group1 -0.724
## rGenHipp -0.006 0.004
## ERActUnsgnd -0.012 0.009 -0.137
## reps1 -0.018 0.013 0.011 -0.016
## shockCond1 -0.006 0.005 0.006 0.001 0.010
## grp1:rGnHpp 0.004 -0.001 -0.728 0.100 -0.010 -0.006
## grp1:ERActU 0.009 -0.008 0.100 -0.732 0.005 0.001 -0.110contrasts(dCleanStandardized$group) = c(1,-1); contrasts(dCleanStandardized$group)## [,1]
## control 1
## stress -1# what about if we model random slope for rep?
# need to add more iterations to converge
summary(glmer(pcorr ~ group*(rGenHipp + ERActUnsigned) + reps + shockCond +
(-1 + rGenHipp|subid) +
(-1 + ERActUnsigned|subid) +
(-1 + reps|subid) +
(1|subid),
control=glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5)),
data=dCleanStandardized, family = "binomial"))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: pcorr ~ group * (rGenHipp + ERActUnsigned) + reps + shockCond +
## (-1 + rGenHipp | subid) + (-1 + ERActUnsigned | subid) +
## (-1 + reps | subid) + (1 | subid)
## Data: dCleanStandardized
## Control:
## glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## AIC BIC logLik deviance df.resid
## 7433.7 7528.8 -3702.8 7405.7 6609
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1754 -0.6746 -0.3556 0.7491 4.9960
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid rGenHipp 0.07025 0.2651
## subid.1 ERActUnsigned 0.04727 0.2174
## subid.2 reps2 0.33529 0.5790
## reps4 0.33943 0.5826 0.96
## subid.3 (Intercept) 0.71092 0.8432
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.44157 0.16076 -2.747 0.00602 **
## group1 0.44801 0.16504 2.715 0.00664 **
## rGenHipp 0.26222 0.05198 5.044 4.56e-07 ***
## ERActUnsigned 0.26385 0.04615 5.717 1.08e-08 ***
## reps1 0.38722 0.03230 11.989 < 2e-16 ***
## shockCond1 0.02025 0.02877 0.704 0.48139
## group1:rGenHipp 0.12117 0.05197 2.331 0.01973 *
## group1:ERActUnsigned -0.03337 0.04614 -0.723 0.46954
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp ERActU reps1 shckC1 gr1:GH
## group1 -0.057
## rGenHipp -0.002 0.005
## ERActUnsgnd -0.008 0.003 -0.110
## reps1 -0.013 0.042 0.007 -0.019
## shockCond1 -0.004 0.003 0.003 0.003 0.008
## grp1:rGnHpp 0.005 -0.003 -0.061 0.036 -0.009 -0.006
## grp1:ERActU 0.004 -0.004 0.036 -0.071 0.007 0.001 -0.110# interactions of reps * group/neural measures?
summary(glmer(pcorr ~ group*(rGenHipp + ERActUnsigned + reps) +
reps:rGenHipp + reps:ERActUnsigned + shockCond +
(-1 + rGenHipp|subid) +
(-1 + ERActUnsigned|subid) +
(1|subid),
control=glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5)),
data=dCleanStandardized, family = "binomial"))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## pcorr ~ group * (rGenHipp + ERActUnsigned + reps) + reps:rGenHipp +
## reps:ERActUnsigned + shockCond + (-1 + rGenHipp | subid) +
## (-1 + ERActUnsigned | subid) + (1 | subid)
## Data: dCleanStandardized
## Control:
## glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## AIC BIC logLik deviance df.resid
## 7428.1 7523.3 -3700.0 7400.1 6609
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2453 -0.6740 -0.3502 0.7529 5.1570
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.07134 0.2671
## subid.1 ERActUnsigned 0.04742 0.2178
## subid.2 (Intercept) 1.04263 1.0211
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.43734 0.16070 -2.721 0.00650 **
## group1 0.44567 0.16070 2.773 0.00555 **
## rGenHipp 0.25695 0.05228 4.915 8.87e-07 ***
## ERActUnsigned 0.27131 0.04629 5.861 4.59e-09 ***
## reps1 0.38622 0.02926 13.200 < 2e-16 ***
## shockCond1 0.01938 0.02877 0.673 0.50066
## group1:rGenHipp 0.12177 0.05222 2.332 0.01971 *
## group1:ERActUnsigned -0.03907 0.04626 -0.845 0.39834
## group1:reps1 0.04103 0.02923 1.404 0.16044
## rGenHipp:reps1 0.03894 0.03115 1.250 0.21121
## ERActUnsigned:reps1 -0.05958 0.03099 -1.922 0.05455 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp ERActU reps1 shckC1 gr1:GH g1:ERA grp1:1
## group1 -0.049
## rGenHipp -0.001 0.005
## ERActUnsgnd -0.009 0.005 -0.111
## reps1 -0.012 0.013 0.004 -0.012
## shockCond1 -0.004 0.005 0.004 0.002 0.010
## grp1:rGnHpp 0.004 -0.001 -0.060 0.036 -0.005 -0.006
## grp1:ERActU 0.004 -0.009 0.036 -0.073 0.000 0.001 -0.108
## group1:rps1 0.012 -0.012 -0.006 0.003 -0.058 -0.005 0.005 -0.014
## rGnHpp:rps1 0.000 -0.002 -0.043 0.030 0.002 -0.018 0.022 0.009 0.032
## ERActUnsg:1 -0.005 -0.001 0.030 -0.073 -0.046 0.006 0.002 0.060 0.017
## rGnH:1
## group1
## rGenHipp
## ERActUnsgnd
## reps1
## shockCond1
## grp1:rGnHpp
## grp1:ERActU
## group1:rps1
## rGnHpp:rps1
## ERActUnsg:1 -0.255# removed Rslope of hipp/vtc to convergeFull model, also controlling for inferior parietal reinstatment
# effect of more things on subsequent memory
deq.1b<-glmer(pcorr ~ group*(rGenHipp + ERActUnsigned + ERActUnsigned_infpar) +
reps + shockCond +
(-1 + rGenHipp|subid) +
(-1 + ERActUnsigned|subid) +
(-1 + ERActUnsigned_infpar|subid) +
(1|subid),
data=dCleanStandardized, family = "binomial",
control=glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5))
)
summary(deq.1b)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## pcorr ~ group * (rGenHipp + ERActUnsigned + ERActUnsigned_infpar) +
## reps + shockCond + (-1 + rGenHipp | subid) + (-1 + ERActUnsigned |
## subid) + (-1 + ERActUnsigned_infpar | subid) + (1 | subid)
## Data: dCleanStandardized
## Control:
## glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## AIC BIC logLik deviance df.resid
## 7407.4 7502.6 -3689.7 7379.4 6609
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9916 -0.6730 -0.3464 0.7374 4.6640
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.06722 0.2593
## subid.1 ERActUnsigned 0.03461 0.1860
## subid.2 ERActUnsigned_infpar 0.03737 0.1933
## subid.3 (Intercept) 1.04625 1.0229
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.44617 0.16098 -2.772 0.005579 **
## group1 0.45305 0.16098 2.814 0.004889 **
## rGenHipp 0.25794 0.05143 5.015 5.3e-07 ***
## ERActUnsigned 0.17655 0.04782 3.692 0.000222 ***
## ERActUnsigned_infpar 0.15867 0.04799 3.306 0.000946 ***
## reps1 0.38542 0.02930 13.152 < 2e-16 ***
## shockCond1 0.02054 0.02886 0.712 0.476725
## group1:rGenHipp 0.12203 0.05142 2.373 0.017636 *
## group1:ERActUnsigned -0.03229 0.04779 -0.676 0.499227
## group1:ERActUnsigned_infpar -0.02038 0.04802 -0.424 0.671227
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp ERActU ERAcU_ reps1 shckC1 gr1:GH
## group1 -0.049
## rGenHipp -0.002 0.005
## ERActUnsgnd -0.005 0.003 -0.098
## ERActUnsgn_ -0.005 0.003 -0.015 -0.344
## reps1 -0.013 0.014 0.006 -0.014 -0.007
## shockCond1 -0.005 0.005 0.004 0.001 0.003 0.011
## grp1:rGnHpp 0.004 -0.001 -0.061 0.034 0.002 -0.012 -0.006
## grp1:ERActU 0.003 -0.005 0.033 -0.026 -0.034 0.006 -0.005 -0.099
## grp1:ERAcU_ 0.003 -0.005 0.003 -0.034 -0.009 -0.003 0.009 -0.015
## gr1:ERAU
## group1
## rGenHipp
## ERActUnsgnd
## ERActUnsgn_
## reps1
## shockCond1
## grp1:rGnHpp
## grp1:ERActU
## grp1:ERAcU_ -0.344Full model, also controlling for angular bold
# effect of more things on subsequent memory
deq.1c<-glmer(pcorr ~ group*(rGenHipp + ERActUnsigned +
ERActUnsigned_infpar + rGenANG) +
reps + shockCond +
(-1 + rGenHipp|subid) +
(-1 + ERActUnsigned|subid) +
(-1 + ERActUnsigned_infpar|subid) +
(-1 + rGenANG|subid) +
(1|subid),
data=dCleanStandardized, family = "binomial",
control=glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5))
)
summary(deq.1c)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## pcorr ~ group * (rGenHipp + ERActUnsigned + ERActUnsigned_infpar +
## rGenANG) + reps + shockCond + (-1 + rGenHipp | subid) + (-1 +
## ERActUnsigned | subid) + (-1 + ERActUnsigned_infpar | subid) +
## (-1 + rGenANG | subid) + (1 | subid)
## Data: dCleanStandardized
## Control:
## glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## AIC BIC logLik deviance df.resid
## 7378.9 7494.5 -3672.4 7344.9 6606
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0248 -0.6645 -0.3405 0.7330 4.2568
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.04350 0.2086
## subid.1 ERActUnsigned 0.03190 0.1786
## subid.2 ERActUnsigned_infpar 0.03932 0.1983
## subid.3 rGenANG 0.05484 0.2342
## subid.4 (Intercept) 1.06627 1.0326
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.45274 0.16252 -2.786 0.005340 **
## group1 0.45935 0.16252 2.827 0.004706 **
## rGenHipp 0.16977 0.05036 3.371 0.000748 ***
## ERActUnsigned 0.17227 0.04755 3.623 0.000291 ***
## ERActUnsigned_infpar 0.15514 0.04885 3.176 0.001494 **
## rGenANG 0.15682 0.05287 2.966 0.003017 **
## reps1 0.38524 0.02949 13.063 < 2e-16 ***
## shockCond1 0.01887 0.02900 0.651 0.515165
## group1:rGenHipp 0.16772 0.05037 3.330 0.000869 ***
## group1:ERActUnsigned -0.02377 0.04755 -0.500 0.617069
## group1:ERActUnsigned_infpar -0.01650 0.04889 -0.338 0.735723
## group1:rGenANG -0.08447 0.05288 -1.597 0.110169
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp ERActU ERAcU_ rGnANG reps1 shckC1 gr1:GH
## group1 -0.049
## rGenHipp 0.000 0.002
## ERActUnsgnd -0.005 0.003 -0.074
## ERActUnsgn_ -0.005 0.003 -0.010 -0.342
## rGenANG -0.004 0.005 -0.305 -0.046 -0.011
## reps1 -0.013 0.014 0.013 -0.011 -0.008 -0.014
## shockCond1 -0.004 0.005 0.005 0.003 0.004 -0.005 0.010
## grp1:rGnHpp 0.001 0.000 -0.066 0.041 0.013 0.019 -0.002 -0.009
## grp1:ERActU 0.002 -0.005 0.041 -0.023 -0.033 -0.014 0.011 -0.005 -0.074
## grp1:ERAcU_ 0.003 -0.005 0.012 -0.033 -0.012 -0.012 -0.001 0.008 -0.010
## grp1:rGnANG 0.005 -0.004 0.020 -0.013 -0.015 -0.054 -0.016 0.005 -0.306
## gr1:ERAU g1:ERAU_
## group1
## rGenHipp
## ERActUnsgnd
## ERActUnsgn_
## rGenANG
## reps1
## shockCond1
## grp1:rGnHpp
## grp1:ERActU
## grp1:ERAcU_ -0.342
## grp1:rGnANG -0.049 -0.010Full model, also controlling for rsp
# effect of more things on subsequent memory
deq.1d<-glmer(pcorr ~ group*(rGenHipp + ERActUnsigned +
ERActUnsigned_infpar +
rGenANG + rGenRSP) +
reps + shockCond +
(-1 + rGenHipp|subid) +
(-1 + ERActUnsigned|subid) +
(-1 + ERActUnsigned_infpar|subid) +
(-1 + rGenANG|subid) +
(-1 + rGenRSP|subid) +
(1|subid),
data=dCleanStandardized, family = "binomial",
control=glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5))
)
summary(deq.1d)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## pcorr ~ group * (rGenHipp + ERActUnsigned + ERActUnsigned_infpar +
## rGenANG + rGenRSP) + reps + shockCond + (-1 + rGenHipp |
## subid) + (-1 + ERActUnsigned | subid) + (-1 + ERActUnsigned_infpar |
## subid) + (-1 + rGenANG | subid) + (-1 + rGenRSP | subid) +
## (1 | subid)
## Data: dCleanStandardized
## Control:
## glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## AIC BIC logLik deviance df.resid
## 7366.3 7502.3 -3663.1 7326.3 6603
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9383 -0.6668 -0.3338 0.7327 4.3757
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.04186 0.2046
## subid.1 ERActUnsigned 0.03044 0.1745
## subid.2 ERActUnsigned_infpar 0.03480 0.1865
## subid.3 rGenANG 0.04821 0.2196
## subid.4 rGenRSP 0.02844 0.1686
## subid.5 (Intercept) 1.07770 1.0381
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.45591 0.16338 -2.790 0.005263 **
## group1 0.46311 0.16338 2.834 0.004590 **
## rGenHipp 0.16191 0.05400 2.998 0.002715 **
## ERActUnsigned 0.17143 0.04869 3.521 0.000430 ***
## ERActUnsigned_infpar 0.14667 0.04869 3.013 0.002590 **
## rGenANG 0.15892 0.05363 2.963 0.003045 **
## rGenRSP 0.02246 0.05711 0.393 0.694145
## reps1 0.38519 0.02957 13.027 < 2e-16 ***
## shockCond1 0.02142 0.02907 0.737 0.461313
## group1:rGenHipp 0.25001 0.05405 4.625 3.74e-06 ***
## group1:ERActUnsigned 0.02260 0.04872 0.464 0.642784
## group1:ERActUnsigned_infpar 0.02050 0.04875 0.420 0.674135
## group1:rGenANG -0.02591 0.05356 -0.484 0.628560
## group1:rGenRSP -0.20658 0.05735 -3.602 0.000316 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itVTC reinstatement ~ hipp
deq.2<-lmer(ERActUnsigned ~ group*(rGenHipp) + reps + shockCond + pcorr +
(-1 + rGenHipp|subid)+
(1|subid),
data=dCleanStandardized)
# uncorrelate random slopes and intercepts
summary(deq.2)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: ERActUnsigned ~ group * (rGenHipp) + reps + shockCond + pcorr +
## (-1 + rGenHipp | subid) + (1 | subid)
## Data: dCleanStandardized
##
## REML criterion at convergence: 18067.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7758 -0.6741 -0.0103 0.6306 4.5744
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.03019 0.1738
## subid.1 (Intercept) 0.00000 0.0000
## Residual 0.88032 0.9383
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.601e-02 1.175e-02 6.577e+03 1.363 0.1728
## group1 -1.689e-02 1.177e-02 6.577e+03 -1.435 0.1514
## rGenHipp 2.658e-01 2.927e-02 4.000e+01 9.079 2.92e-11 ***
## reps1 4.878e-02 1.173e-02 6.590e+03 4.157 3.27e-05 ***
## shockCond1 -3.628e-03 1.155e-02 6.583e+03 -0.314 0.7535
## pcorr1 9.505e-02 1.225e-02 6.605e+03 7.762 9.77e-15 ***
## group1:rGenHipp -5.968e-02 2.924e-02 4.000e+01 -2.041 0.0479 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp reps1 shckC1 pcorr1
## group1 -0.104
## rGenHipp -0.009 0.010
## reps1 -0.029 0.033 -0.009
## shockCond1 -0.021 0.023 0.004 0.003
## pcorr1 0.176 -0.187 -0.052 -0.158 -0.008
## grp1:rGnHpp -0.003 0.003 -0.051 -0.010 -0.004 -0.016fixef(deq.2)## (Intercept) group1 rGenHipp reps1
## 0.016014200 -0.016887630 0.265782366 0.048777760
## shockCond1 pcorr1 group1:rGenHipp
## -0.003627942 0.095051299 -0.059683911fixef(deq.2)[3] # VTC ~ hipp direct effect ## rGenHipp
## 0.2657824fixef(deq.2)[7] # VTC ~ group:hipp## group1:rGenHipp
## -0.05968391# interaction
summary(lmer(ERActUnsigned ~ rGenHipp + reps + shockCond + pcorr +
(-1 + rGenHipp|subid)+
(1|subid),
data=dCleanStandardized %>% filter(group == 'control')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## ERActUnsigned ~ rGenHipp + reps + shockCond + pcorr + (-1 + rGenHipp |
## subid) + (1 | subid)
## Data: dCleanStandardized %>% filter(group == "control")
##
## REML criterion at convergence: 9822.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2808 -0.6935 -0.0286 0.6454 4.4993
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.0282 0.1679
## subid.1 (Intercept) 0.0000 0.0000
## Residual 0.9126 0.9553
## Number of obs: 3553, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.584e-04 1.603e-02 3.527e+03 -0.047 0.9623
## rGenHipp 2.089e-01 3.934e-02 2.100e+01 5.309 2.83e-05 ***
## reps1 4.146e-02 1.635e-02 3.534e+03 2.535 0.0113 *
## shockCond1 -4.082e-03 1.606e-02 3.532e+03 -0.254 0.7993
## pcorr1 8.214e-02 1.666e-02 3.544e+03 4.931 8.55e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rGnHpp reps1 shckC1
## rGenHipp 0.001
## reps1 0.004 -0.019
## shockCond1 0.002 -0.001 0.000
## pcorr1 -0.011 -0.069 -0.167 -0.008summary(lmer(ERActUnsigned ~ rGenHipp + reps + shockCond + pcorr +
(-1 + rGenHipp|subid)+
(1|subid),
data=dCleanStandardized %>% filter(group == 'stress')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## ERActUnsigned ~ rGenHipp + reps + shockCond + pcorr + (-1 + rGenHipp |
## subid) + (1 | subid)
## Data: dCleanStandardized %>% filter(group == "stress")
##
## REML criterion at convergence: 8252
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8485 -0.6430 0.0051 0.6123 3.8242
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.03276 0.1810
## subid.1 (Intercept) 0.00000 0.0000
## Residual 0.84316 0.9182
## Number of obs: 3070, groups: subid, 20
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.871e-02 1.774e-02 3.048e+03 2.182 0.029174 *
## rGenHipp 3.239e-01 4.377e-02 1.890e+01 7.400 5.46e-07 ***
## reps1 5.736e-02 1.682e-02 3.053e+03 3.411 0.000657 ***
## shockCond1 -2.981e-03 1.662e-02 3.049e+03 -0.179 0.857643
## pcorr1 1.120e-01 1.807e-02 3.057e+03 6.197 6.54e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rGnHpp reps1 shckC1
## rGenHipp -0.013
## reps1 -0.057 0.001
## shockCond1 -0.042 0.008 0.007
## pcorr1 0.355 -0.036 -0.147 -0.008## double check with simple effects
# controls:
contrasts(dCleanStandardized$group) = c(0,1); contrasts(dCleanStandardized$group)## [,1]
## control 0
## stress 1summary(lmer(ERActUnsigned ~ group*(rGenHipp) + reps + shockCond + pcorr +
(-1 + rGenHipp|subid)+
(1|subid),
data=dCleanStandardized))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: ERActUnsigned ~ group * (rGenHipp) + reps + shockCond + pcorr +
## (-1 + rGenHipp | subid) + (1 | subid)
## Data: dCleanStandardized
##
## REML criterion at convergence: 18065
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7758 -0.6741 -0.0103 0.6306 4.5744
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.03019 0.1738
## subid.1 (Intercept) 0.00000 0.0000
## Residual 0.88032 0.9383
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.734e-04 1.574e-02 6.576e+03 -0.055 0.9558
## group1 3.378e-02 2.354e-02 6.577e+03 1.435 0.1514
## rGenHipp 2.061e-01 4.031e-02 4.000e+01 5.112 8.45e-06 ***
## reps1 4.878e-02 1.173e-02 6.590e+03 4.157 3.27e-05 ***
## shockCond1 -3.628e-03 1.155e-02 6.583e+03 -0.314 0.7535
## pcorr1 9.505e-02 1.225e-02 6.605e+03 7.762 9.77e-15 ***
## group1:rGenHipp 1.194e-01 5.847e-02 4.000e+01 2.041 0.0479 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp reps1 shckC1 pcorr1
## group1 -0.670
## rGenHipp 0.000 -0.009
## reps1 0.003 -0.033 -0.013
## shockCond1 0.002 -0.023 0.000 0.003
## pcorr1 -0.008 0.187 -0.050 -0.158 -0.008
## grp1:rGnHpp 0.000 0.003 -0.688 0.010 0.004 0.016# stress:
contrasts(dCleanStandardized$group) = c(1,0); contrasts(dCleanStandardized$group)## [,1]
## control 1
## stress 0summary(lmer(ERActUnsigned ~ group*(rGenHipp) + reps + shockCond + pcorr +
(-1 + rGenHipp|subid)+
(1|subid),
data=dCleanStandardized))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: ERActUnsigned ~ group * (rGenHipp) + reps + shockCond + pcorr +
## (-1 + rGenHipp | subid) + (1 | subid)
## Data: dCleanStandardized
##
## REML criterion at convergence: 18065
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7758 -0.6741 -0.0103 0.6306 4.5744
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.03019 0.1738
## subid.1 (Intercept) 0.00000 0.0000
## Residual 0.88032 0.9383
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.290e-02 1.747e-02 6.578e+03 1.883 0.0597 .
## group1 -3.377e-02 2.354e-02 6.577e+03 -1.435 0.1514
## rGenHipp 3.255e-01 4.241e-02 4.000e+01 7.674 2.16e-09 ***
## reps1 4.878e-02 1.173e-02 6.590e+03 4.157 3.27e-05 ***
## shockCond1 -3.628e-03 1.155e-02 6.583e+03 -0.314 0.7535
## pcorr1 9.505e-02 1.225e-02 6.605e+03 7.762 9.77e-15 ***
## group1:rGenHipp -1.194e-01 5.847e-02 4.000e+01 -2.041 0.0479 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp reps1 shckC1 pcorr1
## group1 -0.744
## rGenHipp -0.006 0.005
## reps1 -0.042 0.033 0.001
## shockCond1 -0.030 0.023 0.005 0.003
## pcorr1 0.244 -0.187 -0.025 -0.158 -0.008
## grp1:rGnHpp -0.004 0.003 -0.724 -0.010 -0.004 -0.016contrasts(dCleanStandardized$group) = c(1,-1); contrasts(dCleanStandardized$group)## [,1]
## control 1
## stress -1# look at raw reinstatement by median split of hipp signal
dat = dCleanStandardized %>%
group_by(subid, group) %>%
mutate(hipp_med = ntile(hipp_sig, 2)) %>%
ungroup()
summary(lmer(vtc_logit ~ group + reps + shockCond + pcorr +
(1|subid),
data=dat %>% filter(hipp_med == 1)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: vtc_logit ~ group + reps + shockCond + pcorr + (1 | subid)
## Data: dat %>% filter(hipp_med == 1)
##
## REML criterion at convergence: 16074.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7813 -0.6517 -0.0127 0.6125 4.0562
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.1934 0.4398
## Residual 7.2823 2.6986
## Number of obs: 3320, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.364e-01 8.391e-02 4.100e+01 -2.817 0.007407 **
## group1 2.483e-01 8.298e-02 3.900e+01 2.993 0.004751 **
## reps1 1.790e-01 4.770e-02 3.293e+03 3.754 0.000177 ***
## shockCond1 -5.679e-04 4.694e-02 3.284e+03 -0.012 0.990349
## pcorr1 3.947e-01 5.256e-02 2.916e+03 7.510 7.84e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 reps1 shckC1
## group1 -0.071
## reps1 -0.003 0.028
## shockCond1 -0.008 0.013 0.019
## pcorr1 0.173 -0.093 -0.163 0.003summary(lmer(vtc_logit ~ group + reps + shockCond + pcorr +
(1|subid),
data=dat %>% filter(hipp_med == 2)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: vtc_logit ~ group + reps + shockCond + pcorr + (1 | subid)
## Data: dat %>% filter(hipp_med == 2)
##
## REML criterion at convergence: 16015.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7916 -0.6354 -0.0294 0.6352 4.1681
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.2967 0.5447
## Residual 7.3053 2.7028
## Number of obs: 3303, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.91460 0.09657 40.00000 9.471 9.32e-12 ***
## group1 -0.07251 0.09715 41.00000 -0.746 0.460
## reps1 0.08316 0.04796 3278.00000 1.734 0.083 .
## shockCond1 -0.04257 0.04720 3269.00000 -0.902 0.367
## pcorr1 0.39241 0.05312 2817.00000 7.387 1.97e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 reps1 shckC1
## group1 -0.058
## reps1 -0.029 0.012
## shockCond1 -0.012 0.012 -0.013
## pcorr1 0.035 -0.117 -0.170 -0.016# what about rep effect?
summary(lmer(ERActUnsigned ~ group*(rGenHipp) + reps + shockCond + pcorr +
(-1 + rGenHipp|subid)+
(-1 + reps|subid)+
(1|subid),
data=dCleanStandardized))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: ERActUnsigned ~ group * (rGenHipp) + reps + shockCond + pcorr +
## (-1 + rGenHipp | subid) + (-1 + reps | subid) + (1 | subid)
## Data: dCleanStandardized
##
## REML criterion at convergence: 18065.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7981 -0.6664 -0.0129 0.6315 4.5756
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid rGenHipp 0.030154 0.17365
## subid.1 reps2 0.001565 0.03956
## reps4 0.002186 0.04675 -1.00
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.878495 0.93728
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.595e-02 1.175e-02 3.502e+03 1.358 0.174580
## group1 -1.667e-02 1.177e-02 4.447e+03 -1.417 0.156661
## rGenHipp 2.659e-01 2.926e-02 4.000e+01 9.089 2.84e-11 ***
## reps1 4.879e-02 1.348e-02 4.400e+01 3.619 0.000765 ***
## shockCond1 -3.667e-03 1.154e-02 6.548e+03 -0.318 0.750747
## pcorr1 9.506e-02 1.225e-02 6.427e+03 7.762 9.77e-15 ***
## group1:rGenHipp -5.961e-02 2.922e-02 4.000e+01 -2.040 0.048041 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp reps1 shckC1 pcorr1
## group1 -0.104
## rGenHipp -0.009 0.010
## reps1 -0.002 0.029 -0.008
## shockCond1 -0.021 0.023 0.004 0.003
## pcorr1 0.176 -0.187 -0.052 -0.137 -0.008
## grp1:rGnHpp -0.003 0.003 -0.051 -0.008 -0.004 -0.016Double check: does it matter if pcorr is included?
summary(lmer(ERActUnsigned ~ group*(rGenHipp) + reps + shockCond + pcorr +
(-1 + rGenHipp|subid)+
(1|subid),
data=dCleanStandardized))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: ERActUnsigned ~ group * (rGenHipp) + reps + shockCond + pcorr +
## (-1 + rGenHipp | subid) + (1 | subid)
## Data: dCleanStandardized
##
## REML criterion at convergence: 18067.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7758 -0.6741 -0.0103 0.6306 4.5744
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.03019 0.1738
## subid.1 (Intercept) 0.00000 0.0000
## Residual 0.88032 0.9383
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.601e-02 1.175e-02 6.577e+03 1.363 0.1728
## group1 -1.689e-02 1.177e-02 6.577e+03 -1.435 0.1514
## rGenHipp 2.658e-01 2.927e-02 4.000e+01 9.079 2.92e-11 ***
## reps1 4.878e-02 1.173e-02 6.590e+03 4.157 3.27e-05 ***
## shockCond1 -3.628e-03 1.155e-02 6.583e+03 -0.314 0.7535
## pcorr1 9.505e-02 1.225e-02 6.605e+03 7.762 9.77e-15 ***
## group1:rGenHipp -5.968e-02 2.924e-02 4.000e+01 -2.041 0.0479 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp reps1 shckC1 pcorr1
## group1 -0.104
## rGenHipp -0.009 0.010
## reps1 -0.029 0.033 -0.009
## shockCond1 -0.021 0.023 0.004 0.003
## pcorr1 0.176 -0.187 -0.052 -0.158 -0.008
## grp1:rGnHpp -0.003 0.003 -0.051 -0.010 -0.004 -0.016summary(lmer(ERActUnsigned ~ group*(rGenHipp) + reps + shockCond +
(-1 + rGenHipp|subid)+
(1|subid),
data=dCleanStandardized))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: ERActUnsigned ~ group * (rGenHipp) + reps + shockCond + (-1 +
## rGenHipp | subid) + (1 | subid)
## Data: dCleanStandardized
##
## REML criterion at convergence: 18120.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7816 -0.6721 -0.0054 0.6275 4.4647
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.0313 0.1769
## subid.1 (Intercept) 0.0000 0.0000
## Residual 0.8881 0.9424
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.670e-05 1.161e-02 6.577e+03 -0.002 0.9982
## group1 1.809e-04 1.161e-02 6.577e+03 0.016 0.9876
## rGenHipp 2.777e-01 2.970e-02 4.000e+01 9.348 1.36e-11 ***
## reps1 6.315e-02 1.164e-02 6.591e+03 5.426 5.97e-08 ***
## shockCond1 -2.914e-03 1.160e-02 6.584e+03 -0.251 0.8018
## group1:rGenHipp -5.609e-02 2.970e-02 4.000e+01 -1.888 0.0663 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp reps1 shckC1
## group1 -0.073
## rGenHipp 0.000 0.000
## reps1 -0.001 0.004 -0.017
## shockCond1 -0.020 0.022 0.003 0.002
## grp1:rGnHpp 0.000 0.000 -0.051 -0.012 -0.004summary(lmer(ERActUnsigned ~ group*(rGenHipp*pcorr) + reps + shockCond +
(-1 + rGenHipp|subid)+
(-1 + pcorr|subid)+
(-1 + rGenHipp:pcorr|subid)+
(1|subid),
data=dCleanStandardized))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: ERActUnsigned ~ group * (rGenHipp * pcorr) + reps + shockCond +
## (-1 + rGenHipp | subid) + (-1 + pcorr | subid) + (-1 + rGenHipp:pcorr |
## subid) + (1 | subid)
## Data: dCleanStandardized
##
## REML criterion at convergence: 18066.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8435 -0.6710 -0.0169 0.6265 4.7053
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid rGenHipp 0.000000 0.00000
## subid.1 pcorr0 0.005309 0.07286
## pcorr1 0.009288 0.09637 -1.00
## subid.2 rGenHipp:pcorr0 0.027883 0.16698
## rGenHipp:pcorr1 0.034953 0.18696 1.00
## subid.3 (Intercept) 0.000000 0.00000
## Residual 0.873476 0.93460
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.438e-02 1.280e-02 5.690e+02 1.905 0.0572
## group1 -1.061e-02 1.280e-02 5.710e+02 -0.829 0.4073
## rGenHipp 2.660e-01 2.998e-02 3.900e+01 8.873 6.11e-11
## pcorr1 9.584e-02 1.800e-02 4.300e+01 5.325 3.37e-06
## reps1 4.938e-02 1.171e-02 6.576e+03 4.216 2.52e-05
## shockCond1 -3.824e-03 1.152e-02 6.561e+03 -0.332 0.7400
## rGenHipp:pcorr1 -1.621e-02 1.314e-02 7.710e+02 -1.234 0.2174
## group1:rGenHipp -5.732e-02 2.998e-02 3.900e+01 -1.912 0.0632
## group1:pcorr1 -1.502e-02 1.789e-02 4.200e+01 -0.840 0.4059
## group1:rGenHipp:pcorr1 -1.542e-02 1.314e-02 7.710e+02 -1.174 0.2409
##
## (Intercept) .
## group1
## rGenHipp ***
## pcorr1 ***
## reps1 ***
## shockCond1
## rGenHipp:pcorr1
## group1:rGenHipp .
## group1:pcorr1
## group1:rGenHipp:pcorr1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp pcorr1 reps1 shckC1 rGnH:1 gr1:GH grp1:1
## group1 -0.094
## rGenHipp -0.015 0.020
## pcorr1 0.245 -0.147 -0.038
## reps1 -0.024 0.030 -0.007 -0.107
## shockCond1 -0.020 0.024 0.003 -0.006 0.003
## rGnHpp:pcr1 -0.159 -0.022 0.199 -0.020 -0.004 -0.005
## grp1:rGnHpp 0.021 -0.015 -0.065 -0.002 -0.009 -0.004 -0.100
## grop1:pcrr1 -0.145 0.244 -0.003 -0.086 -0.004 0.000 0.034 -0.039
## grp1:rGnH:1 -0.022 -0.160 -0.100 0.034 -0.006 -0.001 -0.107 0.199 -0.021Mediation of hipp -> pcorr by VTC reinstatement
# run mediation if requested, otherwise just read in data already generated
if (run_mediation == TRUE){
## bootstrapping approach
bootDist_0_hipp<-NULL
bootDist_0_hippxgroup<-NULL
bootDist_1_vtc<-NULL
bootDist_1_vtcxgroup<-NULL
bootDist_1_hipp<-NULL
bootDist_1_hippxgroup<-NULL
bootDist_2_hipp<-NULL
bootDist_2_hippxgroup<-NULL
min_persub <- NULL
for (i in 1:5000) {
print(i)
# randomly sample from data, with replacement
o1<-dCleanStandardized[sample(1:dim(dCleanStandardized)[1],
size=dim(dCleanStandardized)[1],
replace=T),]
min_persub[i] = min(with(o1, table(subid))) # make sure sampling roughly equally across subs
# all trials
beq.0<-glmer(pcorr ~ group*(rGenHipp) + reps + shockCond +
(-1 + rGenHipp|subid) +
(1|subid),
data=o1, family = "binomial")
bootDist_0_hipp[i]<-fixef(beq.0)[3] # overall effect of hipp
bootDist_0_hippxgroup[i]<-fixef(beq.0)[6] # overall effect of hipp:group
beq.1<-glmer(pcorr ~ group*(rGenHipp + ERActUnsigned) + reps + shockCond +
(-1 + rGenHipp|subid) +
(-1 + ERActUnsigned|subid) +
(1|subid),
data=o1, family = "binomial")
bootDist_1_vtc[i]<-fixef(beq.1)[4]
bootDist_1_hipp[i]<-fixef(beq.1)[3]
bootDist_1_hippxgroup[i]<-fixef(beq.1)[7]
bootDist_1_vtcxgroup[i]<-fixef(beq.1)[8]
beq.2<-lmer(ERActUnsigned ~ group*(rGenHipp) + reps + shockCond + pcorr +
(-1 + rGenHipp|subid)+
(1|subid),
data=o1)
bootDist_2_hipp[i]<-fixef(beq.2)[3]
bootDist_2_hippxgroup[i]<-fixef(beq.2)[7]
}
# Save out estimates
boot_est =
data.frame(bootDist_0_hipp,
bootDist_0_hippxgroup,
bootDist_1_vtc,
bootDist_1_vtcxgroup,
bootDist_1_hipp,
bootDist_1_hippxgroup,
bootDist_2_hipp,
bootDist_2_hippxgroup,
min_persub)
head(boot_est)
dim(boot_est) # 5000x9
# write.csv(boot_est, '~/Experiments/AP/stats/pcorr_hippxvtc_bootest.csv')
}
# read in estimates
boot_est = read.csv('~/Experiments/AP/stats/pcorr_hippxvtc_bootest.csv')
head(boot_est)## X bootDist_0_hipp bootDist_0_hippxgroup bootDist_1_vtc
## 1 1 0.3336975 0.03313248 0.3401121
## 2 2 0.3830903 0.07729301 0.2358331
## 3 3 0.3095557 0.10523026 0.2536265
## 4 4 0.3519444 0.11133494 0.2693219
## 5 5 0.3045465 0.12979939 0.2539781
## 6 6 0.3081529 0.12626098 0.3251825
## bootDist_1_vtcxgroup bootDist_1_hipp bootDist_1_hippxgroup
## 1 -0.006185387 0.2442726 0.05085747
## 2 -0.028240283 0.3264301 0.10191093
## 3 -0.063827008 0.2495504 0.13205669
## 4 -0.026769713 0.2815676 0.13341756
## 5 -0.027122569 0.2404858 0.16146134
## 6 -0.017391119 0.2153574 0.16094638
## bootDist_2_hipp bootDist_2_hippxgroup min_persub
## 1 0.2676985 -0.04757805 128
## 2 0.2597329 -0.06407083 112
## 3 0.2376320 -0.05350212 117
## 4 0.2719310 -0.07605860 124
## 5 0.2872711 -0.06754800 125
## 6 0.2602519 -0.04362020 121dim(boot_est) # 5000x10## [1] 5000 10# overall effect
mean(boot_est$bootDist_0_hipp)## [1] 0.3434265quantile(boot_est$bootDist_0_hipp, c(.025, 0.975))## 2.5% 97.5%
## 0.2824646 0.4047669mean(boot_est$bootDist_0_hippxgroup)## [1] 0.09604791quantile(boot_est$bootDist_0_hippxgroup, c(.025, 0.975))## 2.5% 97.5%
## 0.03602254 0.15561271# direct effect
mean(boot_est$bootDist_1_hipp)## [1] 0.2706472quantile(boot_est$bootDist_1_hipp, c(.025, 0.975))## 2.5% 97.5%
## 0.2051427 0.3365692mean(boot_est$bootDist_1_hippxgroup)## [1] 0.1228647quantile(boot_est$bootDist_1_hippxgroup, c(.025, 0.975))## 2.5% 97.5%
## 0.06020048 0.18672030# a
mean(boot_est$bootDist_2_hipp)## [1] 0.2648904quantile(boot_est$bootDist_2_hipp,c(.025, 0.975))## 2.5% 97.5%
## 0.2411793 0.2883050mean(boot_est$bootDist_2_hippxgroup)## [1] -0.06000476quantile(boot_est$bootDist_2_hippxgroup,c(.025, 0.975))## 2.5% 97.5%
## -0.08402030 -0.03689056# b
mean(boot_est$bootDist_1_vtc)## [1] 0.2735032quantile(boot_est$bootDist_1_vtc, c(.025, 0.975))## 2.5% 97.5%
## 0.2086499 0.3356519mean(boot_est$bootDist_1_vtcxgroup)## [1] -0.03461619quantile(boot_est$bootDist_1_vtcxgroup, c(.025, 0.975))## 2.5% 97.5%
## -0.09665609 0.02888107# mediation
IE_hippVIAvtc = boot_est$bootDist_1_vtc * boot_est$bootDist_2_hipp
hist(IE_hippVIAvtc)
mean(IE_hippVIAvtc)## [1] 0.07239975quantile(IE_hippVIAvtc,c(.025, 0.975))## 2.5% 97.5%
## 0.05520369 0.08977096Plots:
pSourcehit ~ hipp BOLD
## Automatically converting the following non-factors to factors: hipp_quintile## group hipp_quintile reps N pcorr pcorr_norm sd
## 1 control 1 weak 22 0.3100913 0.2232141 0.14546483
## 2 control 1 strong 22 0.4742126 0.3873354 0.10106810
## 3 control 2 weak 22 0.3696009 0.2827238 0.11914158
## 4 control 2 strong 22 0.5490386 0.4621614 0.10590166
## 5 control 3 weak 22 0.4173077 0.3304305 0.10339345
## 6 control 3 strong 22 0.5611449 0.4742677 0.12174464
## 7 control 4 weak 22 0.4820399 0.3951627 0.13175145
## 8 control 4 strong 22 0.6357036 0.5488264 0.08697430
## 9 control 5 weak 22 0.5660647 0.4791875 0.14129631
## 10 control 5 strong 22 0.7093450 0.6224678 0.13309681
## 11 stress 1 weak 20 0.1920822 0.2876471 0.14054417
## 12 stress 1 strong 20 0.3288506 0.4244155 0.11218147
## 13 stress 2 weak 20 0.2674672 0.3630321 0.14702833
## 14 stress 2 strong 20 0.3458826 0.4414475 0.12028195
## 15 stress 3 weak 20 0.2658584 0.3614233 0.09625913
## 16 stress 3 strong 20 0.4234249 0.5189898 0.12230139
## 17 stress 4 weak 20 0.2791886 0.3747535 0.12723380
## 18 stress 4 strong 20 0.4375441 0.5331090 0.15923483
## 19 stress 5 weak 20 0.2869778 0.3825427 0.12177475
## 20 stress 5 strong 20 0.4228520 0.5184169 0.12610978
## se ci
## 1 0.03101321 0.06449549
## 2 0.02154779 0.04481109
## 3 0.02540107 0.05282442
## 4 0.02257831 0.04695416
## 5 0.02204356 0.04584209
## 6 0.02595604 0.05397855
## 7 0.02808950 0.05841532
## 8 0.01854298 0.03856225
## 9 0.03012447 0.06264727
## 10 0.02837634 0.05901182
## 11 0.03142663 0.06577670
## 12 0.02508454 0.05250254
## 13 0.03287653 0.06881138
## 14 0.02689586 0.05629369
## 15 0.02152420 0.04505066
## 16 0.02734742 0.05723881
## 17 0.02845034 0.05954725
## 18 0.03560599 0.07452419
## 19 0.02722966 0.05699234
## 20 0.02819900 0.05902119
## Automatically converting the following non-factors to factors: hipp_quintile## group hipp_quintile N pcorr pcorr_norm sd se
## 1 control 1 22 0.3921519 0.3052748 0.10612646 0.02262624
## 2 control 2 22 0.4593198 0.3724426 0.07173378 0.01529369
## 3 control 3 22 0.4892263 0.4023491 0.06871020 0.01464906
## 4 control 4 22 0.5588718 0.4719946 0.08105797 0.01728162
## 5 control 5 22 0.6377048 0.5508277 0.11896710 0.02536387
## 6 stress 1 20 0.2604664 0.3560313 0.10024634 0.02241576
## 7 stress 2 20 0.3066749 0.4022398 0.10647378 0.02380826
## 8 stress 3 20 0.3446416 0.4402065 0.07146471 0.01597999
## 9 stress 4 20 0.3583664 0.4539313 0.09708466 0.02170879
## 10 stress 5 20 0.3549149 0.4504798 0.10223849 0.02286122
## ci
## 1 0.04705384
## 2 0.03180498
## 3 0.03046440
## 4 0.03593909
## 5 0.05274706
## 6 0.04691673
## 7 0.04983127
## 8 0.03344651
## 9 0.04543702
## 10 0.04784909
Plot prop trials/cond by hipp quintile
old_counts = dm %>%
group_by(subid, group, hipp_quintile) %>%
summarise(old_count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), hipp_quintile, fill = list(old_count=0))
d_counts = dm %>%
group_by(subid, group, cond, hipp_quintile) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), cond, hipp_quintile, fill = list(count=0))
d_avg = d_counts %>% left_join(old_counts) %>%
mutate(prop = count/old_count)## Joining, by = c("subid", "group", "hipp_quintile")d_avg## # A tibble: 840 x 7
## subid group cond hipp_quintile count old_count prop
## <fctr> <fctr> <fctr> <int> <dbl> <dbl> <dbl>
## 1 ap100 control itemhit_lo 1 6 31 0.19354839
## 2 ap100 control itemhit_lo 2 5 31 0.16129032
## 3 ap100 control itemhit_lo 3 6 31 0.19354839
## 4 ap100 control itemhit_lo 4 6 31 0.19354839
## 5 ap100 control itemhit_lo 5 2 30 0.06666667
## 6 ap100 control M 1 6 31 0.19354839
## 7 ap100 control M 2 7 31 0.22580645
## 8 ap100 control M 3 6 31 0.19354839
## 9 ap100 control M 4 5 31 0.16129032
## 10 ap100 control M 5 6 30 0.20000000
## # ... with 830 more rowsdfwc = summarySEwithin(d_avg, measurevar="prop", withinvars=c("hipp_quintile",
"cond"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95)## Automatically converting the following non-factors to factors: hipp_quintiledfwc## group hipp_quintile cond N prop prop_norm sd
## 1 control 1 itemhit_lo 22 0.35276098 0.35276098 0.21186145
## 2 control 1 M 22 0.16431512 0.16431512 0.10388456
## 3 control 1 sourcehit 22 0.38426134 0.38426134 0.20434658
## 4 control 1 sourcemiss_hi 22 0.09866256 0.09866256 0.07197340
## 5 control 2 itemhit_lo 22 0.34139656 0.34139656 0.22308178
## 6 control 2 M 22 0.11880994 0.11880994 0.08109151
## 7 control 2 sourcehit 22 0.44540642 0.44540642 0.23806021
## 8 control 2 sourcemiss_hi 22 0.09438709 0.09438709 0.09311189
## 9 control 3 itemhit_lo 22 0.30301830 0.30301830 0.19837352
## 10 control 3 M 22 0.11717737 0.11717737 0.09854851
## 11 control 3 sourcehit 22 0.48988295 0.48988295 0.25286916
## 12 control 3 sourcemiss_hi 22 0.08992137 0.08992137 0.05523477
## 13 control 4 itemhit_lo 22 0.25178994 0.25178994 0.20635251
## 14 control 4 M 22 0.11247039 0.11247039 0.10665321
## 15 control 4 sourcehit 22 0.55953570 0.55953570 0.26064033
## 16 control 4 sourcemiss_hi 22 0.07620396 0.07620396 0.04890686
## 17 control 5 itemhit_lo 22 0.20133982 0.20133982 0.23565830
## 18 control 5 M 22 0.06469735 0.06469735 0.08056454
## 19 control 5 sourcehit 22 0.64590501 0.64590501 0.28732515
## 20 control 5 sourcemiss_hi 22 0.08805782 0.08805782 0.08559806
## 21 stress 1 itemhit_lo 20 0.46099172 0.46099172 0.22253005
## 22 stress 1 M 20 0.20785387 0.20785387 0.18838795
## 23 stress 1 sourcehit 20 0.25600609 0.25600609 0.16566487
## 24 stress 1 sourcemiss_hi 20 0.07514832 0.07514832 0.05903036
## 25 stress 2 itemhit_lo 20 0.40819940 0.40819940 0.20386214
## 26 stress 2 M 20 0.19139423 0.19139423 0.12983854
## 27 stress 2 sourcehit 20 0.30132944 0.30132944 0.14377106
## 28 stress 2 sourcemiss_hi 20 0.09907693 0.09907693 0.07491831
## 29 stress 3 itemhit_lo 20 0.38554256 0.38554256 0.22360851
## 30 stress 3 M 20 0.19122182 0.19122182 0.14239897
## 31 stress 3 sourcehit 20 0.34325649 0.34325649 0.17978893
## 32 stress 3 sourcemiss_hi 20 0.07997913 0.07997913 0.06869600
## 33 stress 4 itemhit_lo 20 0.33727768 0.33727768 0.17289819
## 34 stress 4 M 20 0.19303524 0.19303524 0.16082421
## 35 stress 4 sourcehit 20 0.36347673 0.36347673 0.18176464
## 36 stress 4 sourcemiss_hi 20 0.10621035 0.10621035 0.07378585
## 37 stress 5 itemhit_lo 20 0.39068835 0.39068835 0.23510317
## 38 stress 5 M 20 0.16467990 0.16467990 0.16850700
## 39 stress 5 sourcehit 20 0.36220528 0.36220528 0.20178193
## 40 stress 5 sourcemiss_hi 20 0.08242646 0.08242646 0.07544436
## se ci
## 1 0.04516901 0.09393410
## 2 0.02214826 0.04605984
## 3 0.04356684 0.09060220
## 4 0.01534478 0.03191122
## 5 0.04756120 0.09890892
## 6 0.01728877 0.03595396
## 7 0.05075461 0.10554998
## 8 0.01985152 0.04128350
## 9 0.04229338 0.08795389
## 10 0.02101061 0.04369396
## 11 0.05391189 0.11211590
## 12 0.01177609 0.02448972
## 13 0.04399450 0.09149158
## 14 0.02273854 0.04728738
## 15 0.05556870 0.11556145
## 16 0.01042698 0.02168409
## 17 0.05024252 0.10448504
## 18 0.01717642 0.03572032
## 19 0.06125793 0.12739283
## 20 0.01824957 0.03795205
## 21 0.04975923 0.10414727
## 22 0.04212483 0.08816828
## 23 0.03704379 0.07753354
## 24 0.01319959 0.02762706
## 25 0.04558496 0.09541042
## 26 0.02903278 0.06076631
## 27 0.03214819 0.06728693
## 28 0.01675224 0.03506285
## 29 0.05000038 0.10465200
## 30 0.03184138 0.06664477
## 31 0.04020203 0.08414381
## 32 0.01536089 0.03215072
## 33 0.03866121 0.08091884
## 34 0.03596139 0.07526805
## 35 0.04064381 0.08506847
## 36 0.01649902 0.03453284
## 37 0.05257067 0.11003167
## 38 0.03767931 0.07886370
## 39 0.04511981 0.09443685
## 40 0.01686987 0.03530905p2 = ggplot(dfwc, aes(x=hipp_quintile, y=prop, group=cond, color=cond)) +
facet_grid(.~group)+
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=prop-se,
ymax=prop+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
xlab('Hippocampal activity (quintiles)')+
ylab('p Old trials')+
scale_color_brewer(palette = 'Set1', guide = guide_legend(title = NULL))
ggsave(p2,
filename ='~/Experiments/AP/figs/pOLDXhippxreps_quintile_allOLD_wholehipp.jpg',
dpi=150, width = 8, height=4.4)
p2
Plot prop trials/cond by VTC quintile
old_counts = dm %>%
group_by(subid, group, vtc_quintile) %>%
summarise(old_count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), vtc_quintile, fill = list(old_count=0))
d_counts = dm %>%
group_by(subid, group, cond, vtc_quintile) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), cond, vtc_quintile, fill = list(count=0))
d_avg = d_counts %>% left_join(old_counts) %>%
mutate(prop = count/old_count)## Joining, by = c("subid", "group", "vtc_quintile")d_avg## # A tibble: 840 x 7
## subid group cond vtc_quintile count old_count prop
## <fctr> <fctr> <fctr> <int> <dbl> <dbl> <dbl>
## 1 ap100 control itemhit_lo 1 6 31 0.19354839
## 2 ap100 control itemhit_lo 2 3 31 0.09677419
## 3 ap100 control itemhit_lo 3 6 31 0.19354839
## 4 ap100 control itemhit_lo 4 4 31 0.12903226
## 5 ap100 control itemhit_lo 5 6 30 0.20000000
## 6 ap100 control M 1 13 31 0.41935484
## 7 ap100 control M 2 6 31 0.19354839
## 8 ap100 control M 3 4 31 0.12903226
## 9 ap100 control M 4 3 31 0.09677419
## 10 ap100 control M 5 4 30 0.13333333
## # ... with 830 more rowsdfwc = summarySEwithin(d_avg, measurevar="prop", withinvars=c("vtc_quintile",
"cond"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95)## Automatically converting the following non-factors to factors: vtc_quintiledfwc## group vtc_quintile cond N prop prop_norm sd
## 1 control 1 itemhit_lo 22 0.34331686 0.34331686 0.19483767
## 2 control 1 M 22 0.23403553 0.23403553 0.13586695
## 3 control 1 sourcehit 22 0.36563626 0.36563626 0.24880531
## 4 control 1 sourcemiss_hi 22 0.05701135 0.05701135 0.06365228
## 5 control 2 itemhit_lo 22 0.28590814 0.28590814 0.21630470
## 6 control 2 M 22 0.13556170 0.13556170 0.10767858
## 7 control 2 sourcehit 22 0.51957945 0.51957945 0.25747819
## 8 control 2 sourcemiss_hi 22 0.05895071 0.05895071 0.05719109
## 9 control 3 itemhit_lo 22 0.30673921 0.30673921 0.23504879
## 10 control 3 M 22 0.09083906 0.09083906 0.07779137
## 11 control 3 sourcehit 22 0.52350159 0.52350159 0.28035662
## 12 control 3 sourcemiss_hi 22 0.07892014 0.07892014 0.05765584
## 13 control 4 itemhit_lo 22 0.29598970 0.29598970 0.21992697
## 14 control 4 M 22 0.07073550 0.07073550 0.06627701
## 15 control 4 sourcehit 22 0.53870328 0.53870328 0.25128105
## 16 control 4 sourcemiss_hi 22 0.09457152 0.09457152 0.07711294
## 17 control 5 itemhit_lo 22 0.21835402 0.21835402 0.21698316
## 18 control 5 M 22 0.04497705 0.04497705 0.06089267
## 19 control 5 sourcehit 22 0.57749583 0.57749583 0.20780342
## 20 control 5 sourcemiss_hi 22 0.15917310 0.15917310 0.10879425
## 21 stress 1 itemhit_lo 20 0.40488787 0.40488787 0.21068485
## 22 stress 1 M 20 0.29705376 0.29705376 0.18815374
## 23 stress 1 sourcehit 20 0.21504414 0.21504414 0.13838350
## 24 stress 1 sourcemiss_hi 20 0.08301424 0.08301424 0.07430011
## 25 stress 2 itemhit_lo 20 0.43379585 0.43379585 0.21568944
## 26 stress 2 M 20 0.19439869 0.19439869 0.15149622
## 27 stress 2 sourcehit 20 0.30783653 0.30783653 0.18983027
## 28 stress 2 sourcemiss_hi 20 0.06396894 0.06396894 0.05926241
## 29 stress 3 itemhit_lo 20 0.40157376 0.40157376 0.21571099
## 30 stress 3 M 20 0.19380016 0.19380016 0.18209700
## 31 stress 3 sourcehit 20 0.32327140 0.32327140 0.14879653
## 32 stress 3 sourcemiss_hi 20 0.08135468 0.08135468 0.05823998
## 33 stress 4 itemhit_lo 20 0.39428115 0.39428115 0.21296860
## 34 stress 4 M 20 0.14986543 0.14986543 0.13959832
## 35 stress 4 sourcehit 20 0.35673821 0.35673821 0.18502669
## 36 stress 4 sourcemiss_hi 20 0.09911521 0.09911521 0.08466166
## 37 stress 5 itemhit_lo 20 0.34917543 0.34917543 0.20832641
## 38 stress 5 M 20 0.11108045 0.11108045 0.14443430
## 39 stress 5 sourcehit 20 0.42471654 0.42471654 0.20082490
## 40 stress 5 sourcemiss_hi 20 0.11502758 0.11502758 0.08851722
## se ci
## 1 0.04153953 0.08638618
## 2 0.02896693 0.06024003
## 3 0.05304547 0.11031410
## 4 0.01357071 0.02822184
## 5 0.04611632 0.09590413
## 6 0.02295715 0.04774201
## 7 0.05489454 0.11415944
## 8 0.01219318 0.02535711
## 9 0.05011257 0.10421479
## 10 0.01658518 0.03449076
## 11 0.05977223 0.12430316
## 12 0.01229227 0.02556317
## 13 0.04688859 0.09751015
## 14 0.01413031 0.02938558
## 15 0.05357330 0.11141178
## 16 0.01644053 0.03418996
## 17 0.04626096 0.09620494
## 18 0.01298236 0.02699830
## 19 0.04430384 0.09213488
## 20 0.02319501 0.04823667
## 21 0.04711056 0.09860354
## 22 0.04207246 0.08805866
## 23 0.03094349 0.06476547
## 24 0.01661401 0.03477352
## 25 0.04822962 0.10094576
## 26 0.03387558 0.07090241
## 27 0.04244734 0.08884330
## 28 0.01325148 0.02773566
## 29 0.04823444 0.10095585
## 30 0.04071813 0.08522402
## 31 0.03327192 0.06963892
## 32 0.01302286 0.02725715
## 33 0.04762123 0.09967237
## 34 0.03121513 0.06533403
## 35 0.04137323 0.08659516
## 36 0.01893092 0.03962288
## 37 0.04658320 0.09749976
## 38 0.03229649 0.06759733
## 39 0.04490581 0.09398895
## 40 0.01979305 0.04142734p2 = ggplot(dfwc, aes(x=vtc_quintile, y=prop, group=cond, color=cond)) +
facet_grid(.~group)+
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=prop-se,
ymax=prop+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
xlab('Reinstatement strength (quintiles)')+
ylab('p Old trials')+
scale_color_brewer(palette = 'Set1', guide = guide_legend(title = NULL))
ggsave(p2,
filename ='~/Experiments/AP/figs/pOLDXvtc_quintile_allOLD_wholehipp.jpg',
dpi=150, width = 8, height=4.4)
p2
z-scored hipp by condition
d_avg = dm %>%
group_by(subid, group, cond) %>%
summarise(hipp_sig_z = mean(hipp_sig_z))
d_avg## Source: local data frame [167 x 4]
## Groups: subid, group [?]
##
## # A tibble: 167 x 4
## subid group cond hipp_sig_z
## <fctr> <fctr> <fctr> <dbl>
## 1 ap100 control itemhit_lo -0.22153305
## 2 ap100 control M -0.08858725
## 3 ap100 control sourcehit 0.11669585
## 4 ap100 control sourcemiss_hi -0.16304960
## 5 ap101 control itemhit_lo -0.49336910
## 6 ap101 control M -0.61006589
## 7 ap101 control sourcehit 0.29371383
## 8 ap101 control sourcemiss_hi 0.25060666
## 9 ap102 control itemhit_lo -0.29776794
## 10 ap102 control M -0.05133410
## # ... with 157 more rowsdfwc = summarySEwithin(d_avg, measurevar="hipp_sig_z", withinvars=c("cond"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95)
dfwc## group cond N hipp_sig_z hipp_sig_z_norm sd
## 1 control itemhit_lo 21 -0.245061388 -0.19636229 0.2544894
## 2 control M 22 -0.355365623 -0.29596648 0.2065093
## 3 control sourcehit 22 0.167099187 0.22649833 0.3044788
## 4 control sourcemiss_hi 22 0.010926767 0.07032591 0.4464622
## 5 stress itemhit_lo 20 0.001801552 -0.05998625 0.3932803
## 6 stress M 20 -0.112207534 -0.17399534 0.2557633
## 7 stress sourcehit 20 0.153534276 0.09174647 0.2900263
## 8 stress sourcemiss_hi 20 0.015299366 -0.04648844 0.2960797
## se ci
## 1 0.05553415 0.11584220
## 2 0.04402792 0.09156108
## 3 0.06491510 0.13499835
## 4 0.09518606 0.19795025
## 5 0.08794015 0.18406086
## 6 0.05719041 0.11970090
## 7 0.06485185 0.13573648
## 8 0.06620544 0.13856959p2 = ggplot(dfwc, aes(x=cond, y=hipp_sig_z, group=group, color=group)) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=hipp_sig_z-se,
ymax=hipp_sig_z+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
xlab('condition')+
ylab('Hippocampal signal\n(z-scored)')+
scale_color_manual(values = palette, guide = guide_legend(title = NULL))
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/hippsigzxcond_allOLD_wholehipp.jpg',
dpi=150, width = 8, height=4.4)
p2
pSourcehit ~ VTC
## Automatically converting the following non-factors to factors: vtc_quintile## group vtc_quintile reps N pcorr pcorr_norm sd
## 1 control 1 weak 22 0.2762090 0.1890566 0.17157145
## 2 control 1 strong 22 0.4904741 0.4033218 0.17586139
## 3 control 2 weak 22 0.4228444 0.3356921 0.10603623
## 4 control 2 strong 22 0.6190047 0.5318524 0.12346851
## 5 control 3 weak 22 0.4418255 0.3546731 0.08492566
## 6 control 3 strong 22 0.6196368 0.5324844 0.12585047
## 7 control 4 weak 22 0.4758510 0.3886986 0.11445124
## 8 control 4 strong 22 0.5938993 0.5067469 0.10544557
## 9 control 5 weak 22 0.4933183 0.4061659 0.13056869
## 10 control 5 strong 22 0.6443695 0.5572171 0.17127927
## 11 stress 1 weak 20 0.1541300 0.2499976 0.13475615
## 12 stress 1 strong 20 0.2882107 0.3840783 0.12691446
## 13 stress 2 weak 20 0.2374587 0.3333263 0.11581727
## 14 stress 2 strong 20 0.3802869 0.4761545 0.12746713
## 15 stress 3 weak 20 0.2796452 0.3755128 0.12450140
## 16 stress 3 strong 20 0.3757698 0.4716374 0.11602903
## 17 stress 4 weak 20 0.3031382 0.3990058 0.11581835
## 18 stress 4 strong 20 0.4125594 0.5084270 0.10881162
## 19 stress 5 weak 20 0.3226128 0.4184804 0.12802254
## 20 stress 5 strong 20 0.4934211 0.5892888 0.13998358
## se ci
## 1 0.03657916 0.07607052
## 2 0.03749377 0.07797257
## 3 0.02260700 0.04701383
## 4 0.02632357 0.05474287
## 5 0.01810621 0.03765393
## 6 0.02683141 0.05579897
## 7 0.02440109 0.05074484
## 8 0.02248107 0.04675195
## 9 0.02783734 0.05789091
## 10 0.03651686 0.07594097
## 11 0.03013239 0.06306782
## 12 0.02837894 0.05939779
## 13 0.02589753 0.05420415
## 14 0.02850252 0.05965645
## 15 0.02783936 0.05826845
## 16 0.02594488 0.05430326
## 17 0.02589777 0.05420466
## 18 0.02433102 0.05092541
## 19 0.02862671 0.05991640
## 20 0.03130128 0.06551433
## Automatically converting the following non-factors to factors: vtc_quintile## group vtc_quintile N pcorr pcorr_norm sd se
## 1 control 1 22 0.3833416 0.2961892 0.11926155 0.02542665
## 2 control 2 22 0.5209246 0.4337722 0.08492980 0.01810709
## 3 control 3 22 0.5307311 0.4435788 0.08382327 0.01787118
## 4 control 4 22 0.5348751 0.4477227 0.07459251 0.01590318
## 5 control 5 22 0.5688439 0.4816915 0.12504708 0.02666013
## 6 stress 1 20 0.2211704 0.3170380 0.11358762 0.02539896
## 7 stress 2 20 0.3088728 0.4047404 0.10959979 0.02450726
## 8 stress 3 20 0.3277075 0.4235751 0.07874369 0.01760762
## 9 stress 4 20 0.3578488 0.4537164 0.07454267 0.01666825
## 10 stress 5 20 0.4080170 0.5038846 0.10545304 0.02358002
## ci
## 1 0.05287761
## 2 0.03765577
## 3 0.03716516
## 4 0.03307247
## 5 0.05544277
## 6 0.05316064
## 7 0.05129428
## 8 0.03685318
## 9 0.03488704
## 10 0.04935354
VTC ~ hipp BOLD
dm_avg = dm %>%
group_by(subid, group, hipp_quintile, reps) %>%
summarise(vtc_logit = mean_(vtc_logit), count = n())
dim(dm_avg)## [1] 420 6dfwc = summarySEwithin(dm_avg, measurevar="vtc_logit",
withinvars=c("hipp_quintile", "reps"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95) %>%
mutate(reps=ifelse(reps == "4", "strong", "weak"),
reps=factor(reps, levels=c('weak', 'strong')))## Automatically converting the following non-factors to factors: hipp_quintiledfwc## group hipp_quintile reps N vtc_logit vtc_logit_norm sd
## 1 control 1 weak 22 -0.74440752 -0.90008242 1.0908007
## 2 control 1 strong 22 -0.28463077 -0.44030568 1.2030404
## 3 control 2 weak 22 -0.08859177 -0.24426667 0.9251402
## 4 control 2 strong 22 0.45782397 0.30214907 0.7824452
## 5 control 3 weak 22 0.20458897 0.04891407 0.7935526
## 6 control 3 strong 22 0.63050493 0.47483003 0.7368128
## 7 control 4 weak 22 0.76720258 0.61152767 0.9012212
## 8 control 4 strong 22 0.80915512 0.65348022 0.6356722
## 9 control 5 weak 22 1.37357101 1.21789610 0.8363582
## 10 control 5 strong 22 1.24771890 1.09204400 1.1796830
## 11 stress 1 weak 20 -1.31163457 -1.14039218 1.0981805
## 12 stress 1 strong 20 -0.98405795 -0.81281555 1.0673480
## 13 stress 2 weak 20 -0.63686645 -0.46562405 0.6786301
## 14 stress 2 strong 20 -0.25150904 -0.08026664 0.6901484
## 15 stress 3 weak 20 -0.12550461 0.04573778 0.7387358
## 16 stress 3 strong 20 0.28418265 0.45542505 0.8139988
## 17 stress 4 weak 20 0.40468520 0.57592760 0.7157759
## 18 stress 4 strong 20 0.83189612 1.00313851 1.0003450
## 19 stress 5 weak 20 1.22920014 1.40044253 1.0198885
## 20 stress 5 strong 20 1.66337095 1.83461335 1.2142923
## se ci
## 1 0.2325595 0.4836339
## 2 0.2564891 0.5333982
## 3 0.1972406 0.4101842
## 4 0.1668179 0.3469168
## 5 0.1691860 0.3518415
## 6 0.1570890 0.3266845
## 7 0.1921410 0.3995791
## 8 0.1355258 0.2818413
## 9 0.1783122 0.3708205
## 10 0.2515093 0.5230422
## 11 0.2455606 0.5139643
## 12 0.2386663 0.4995343
## 13 0.1517463 0.3176087
## 14 0.1543219 0.3229994
## 15 0.1651863 0.3457390
## 16 0.1820157 0.3809632
## 17 0.1600524 0.3349934
## 18 0.2236839 0.4681759
## 19 0.2280540 0.4773225
## 20 0.2715240 0.5683063p2 = ggplot(dfwc, aes(x=hipp_quintile, y=vtc_logit, group=group, color=group)) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=vtc_logit-se,
ymax=vtc_logit+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
facet_grid(. ~ reps) +
ylab('Reinstatement strength (logits)')+
xlab('Hippocampal activity (quintiles)')+
scale_color_manual(values=palette, guide = guide_legend(title = NULL))
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/logitXhippxreps_quintile_allOLD_wholehipp.jpg',
dpi=150, width = 8, height=4.4)
p2
## collapse across rep
dm_avg = dm %>%
group_by(subid, group, hipp_quintile, reps) %>%
summarise(vtc_logit = mean_(vtc_logit), count = n()) %>%
group_by(subid, group, hipp_quintile) %>%
summarise(vtc_logit = mean_(vtc_logit), count = n())
dfwc = summarySEwithin(dm_avg, measurevar="vtc_logit",
withinvars=c("hipp_quintile"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95)## Automatically converting the following non-factors to factors: hipp_quintiledfwc## group hipp_quintile N vtc_logit vtc_logit_norm sd
## 1 control 1 22 -0.51451915 -0.6701941 1.0612817
## 2 control 2 22 0.18461610 0.0289412 0.7474041
## 3 control 3 22 0.41754695 0.2618720 0.5459523
## 4 control 4 22 0.78817885 0.6325039 0.6302229
## 5 control 5 22 1.31064496 1.1549701 0.9216787
## 6 stress 1 20 -1.14784626 -0.9766039 0.9643338
## 7 stress 2 20 -0.44418774 -0.2729453 0.5264578
## 8 stress 3 20 0.07933902 0.2505814 0.3588214
## 9 stress 4 20 0.61829066 0.7895331 0.5503767
## 10 stress 5 20 1.44628554 1.6175279 1.0794175
## se ci
## 1 0.22626603 0.4705460
## 2 0.15934710 0.3313804
## 3 0.11639743 0.2420617
## 4 0.13436398 0.2794252
## 5 0.19650255 0.4086494
## 6 0.21563160 0.4513221
## 7 0.11771953 0.2463898
## 8 0.08023491 0.1679336
## 9 0.12306797 0.2575842
## 10 0.24136509 0.5051829p2 = ggplot(dfwc, aes(x=hipp_quintile, y=vtc_logit, group=group, color=group)) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=vtc_logit-se,
ymax=vtc_logit+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('Reinstatement strength')+
xlab('Hippocampal activity (quintiles)')+
scale_color_manual(values=palette,
guide = guide_legend(title = NULL))+
theme_classic(base_size = 20)+
theme(legend.title=element_blank(),
legend.position="none",
strip.background = element_rect(colour="white", fill="white"))
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/logitXhipp_quintile_allOLD_wholehipp.jpg',
dpi=600, width = 4.65, height=4)
p2
## logits (z-scored within subj)
dm_avg = dm %>%
group_by(subid, group, hipp_quintile, reps) %>%
summarise(vtc_logit = mean_(vtc_logit_z), count = n()) %>%
group_by(subid, group, hipp_quintile) %>%
summarise(vtc_logit = mean_(vtc_logit), count = n())
dfwc = summarySEwithin(dm_avg, measurevar="vtc_logit", withinvars=c("hipp_quintile"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95)## Automatically converting the following non-factors to factors: hipp_quintiledfwc## group hipp_quintile N vtc_logit vtc_logit_norm sd
## 1 control 1 22 -0.309268431 -0.310953910 0.3425772
## 2 control 2 22 -0.077340217 -0.079025696 0.2556196
## 3 control 3 22 -0.006171192 -0.007856671 0.1767599
## 4 control 4 22 0.112310524 0.110625044 0.2044810
## 5 control 5 22 0.296760225 0.295074746 0.2839917
## 6 stress 1 20 -0.432574439 -0.430720412 0.3183049
## 7 stress 2 20 -0.193565564 -0.191711536 0.1674465
## 8 stress 3 20 -0.014512115 -0.012658088 0.1287906
## 9 stress 4 20 0.179418496 0.181272524 0.1782260
## 10 stress 5 20 0.459826998 0.461681026 0.3452719
## se ci
## 1 0.07303769 0.15189020
## 2 0.05449829 0.11333539
## 3 0.03768533 0.07837093
## 4 0.04359550 0.09066181
## 5 0.06054723 0.12591486
## 6 0.07117514 0.14897128
## 7 0.03744217 0.07836736
## 8 0.02879845 0.06027585
## 9 0.03985255 0.08341234
## 10 0.07720514 0.16159222p2 = ggplot(dfwc, aes(x=hipp_quintile, y=vtc_logit, group=group, color=group)) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=vtc_logit-se,
ymax=vtc_logit+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('Reinstatement strength\n(z-scored logits)')+
xlab('Hippocampal activity (quintiles)')+
scale_color_manual(values=palette, guide = guide_legend(title = NULL))
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/zscoredlogitXhipp_quintile_allOLD_wholehipp.jpg',
dpi=150, width = 6, height=4)
p2
Look at hipp raw timecourse across all old trials
# Read in hipp BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-hippocampus.csv')
roi_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-hippocampus.csv')
# Take timepoints of interest for old trials, and collapse across hemispheres
roi_f = bind_rows("lh" = roi_lh, "rh" = roi_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M')) %>%
group_by(subid, hemi, run, onset, time) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset, time) %>%
summarise(hipp_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characterroi_f = roi_f %>% inner_join(d, by=c('subid', 'run', 'onset')) %>%
mutate(subid = factor(subid),
cond = factor(cond)) %>%
left_join(dm %>% dplyr::select(subid, run, onset, hipp_quintile))## Warning in inner_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vector## Joining, by = c("subid", "run", "onset")roi_f %>% group_by(group, subid) %>% summarise(n()) %>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 22
## 2 stress 20head(roi_f)## # A tibble: 6 x 53
## subid run onset time hipp_sig X.x trial duration shockCond
## <fctr> <dbl> <dbl> <int> <dbl> <int> <int> <dbl> <fctr>
## 1 ap100 1 0.0191 0 -0.25848007 0 1 10.6661 safe
## 2 ap100 1 0.0191 2 -0.29558563 0 1 10.6661 safe
## 3 ap100 1 0.0191 4 0.06634903 0 1 10.6661 safe
## 4 ap100 1 0.0191 6 0.38894272 0 1 10.6661 safe
## 5 ap100 1 0.0191 8 0.60944748 0 1 10.6661 safe
## 6 ap100 1 0.0191 10 0.03682709 0 1 10.6661 safe
## # ... with 44 more variables: shockTrial <int>, target <fctr>,
## # associate <fctr>, resp <fctr>, acc <fctr>, accSpec <fctr>,
## # respRT <dbl>, remove <lgl>, anxious <int>, happy <int>, safe <int>,
## # stressed <int>, life_stress <int>, sleep <dbl>, cond_orig <fctr>,
## # shock_and_post <int>, conf <fctr>, onset_adj <dbl>,
## # mem_conditions <fctr>, obj_conditions <fctr>, onset_code <fctr>,
## # rt_z <dbl>, mem_conditions_byrep <fctr>, onset_code_byrep <fctr>,
## # img_type <fctr>, X.y <int>, index <int>, group <fctr>,
## # condition <fctr>, category <fctr>, X0 <dbl>, X2 <dbl>, X4 <dbl>,
## # X6 <dbl>, X8 <dbl>, X10 <dbl>, X12 <dbl>, avg_logit <dbl>,
## # cond <fctr>, reps <int>, pcorr <fctr>, vtc_logit <dbl>,
## # imgType <fctr>, hipp_quintile <int>with(roi_f, table(cond, condition))## condition
## cond CR-0 FA-0 itemhit_lo-2 itemhit_lo-4 M-2 M-4
## itemhit_lo 0 0 8862 6930 0 0
## M 0 0 0 0 4424 2471
## sourcehit 0 0 0 0 0 0
## sourcemiss_hi 0 0 0 0 0 0
## condition
## cond sourcehit-2 sourcehit-4 sourcemiss_hi-2 sourcemiss_hi-4
## itemhit_lo 0 0 0 0
## M 0 0 0 0
## sourcehit 7924 11655 0 0
## sourcemiss_hi 0 0 1946 2149ggplot(roi_f %>% group_by(subid, cond, time) %>%
summarise(hipp_sig = mean(hipp_sig)) %>%
group_by(time, cond) %>%
summarise(mean(hipp_sig)),
aes(x=time, y=`mean(hipp_sig)`, color=cond, group=cond)) +
geom_line()
Break down by quintile (from above plotting)
hipp_avg = roi_f %>%
group_by(subid, group, hipp_quintile, time) %>%
summarise(hipp_sig = mean(hipp_sig)) %>%
group_by(group, hipp_quintile, time) %>%
summarise(mean = mean(hipp_sig), se = std.error(hipp_sig), n())
hipp_avg ## Source: local data frame [70 x 6]
## Groups: group, hipp_quintile [?]
##
## # A tibble: 70 x 6
## group hipp_quintile time mean se `n()`
## <fctr> <int> <int> <dbl> <dbl> <int>
## 1 control 1 0 -0.09739443 0.02388204 22
## 2 control 1 2 -0.21292476 0.02862795 22
## 3 control 1 4 -0.35297557 0.02968476 22
## 4 control 1 6 -0.40003707 0.02731876 22
## 5 control 1 8 -0.38541653 0.02467327 22
## 6 control 1 10 -0.33272448 0.02171856 22
## 7 control 1 12 -0.23328205 0.02678441 22
## 8 control 2 0 -0.01681712 0.01663057 22
## 9 control 2 2 -0.08682270 0.01654324 22
## 10 control 2 4 -0.11314266 0.01639213 22
## # ... with 60 more rowsblues <- brewer.pal(6, "Blues")[2:6]
p2 = ggplot(hipp_avg, aes(x=factor(time), y=mean, group=factor(hipp_quintile), color=factor(hipp_quintile))) +
facet_grid(.~group) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=mean-se,
ymax=mean+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('% signal change') +
xlab('Time post-stimulus onset (s)') +
guides(color=guide_legend(title="Quintile")) +
scale_color_manual(values=blues)
ggsave(p2,
filename ='~/Experiments/AP/figs/hipp_bytimeXhippQuintile.jpg',
dpi=300, width = 8, height=4)
p2
p2 = ggplot(hipp_avg %>% filter(hipp_quintile == 5),
aes(x=factor(time), y=mean, group=group, color=group)) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=mean-se,
ymax=mean+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('% signal change') +
xlab('Time post-stimulus onset (s)') +
guides(color=FALSE) +
scale_color_manual(values=palette)
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/hipp_bytimeX5thquintile.jpg',
dpi=300, width = 5, height=4)
p2
2b) Signal variation for HC associative hits
# standardize continuous vars
dCleanStandardized_HC = dm %>% filter(pcorr == 1)
dCleanStandardized_HC = dCleanStandardized_HC %>%
group_by(subid) %>%
mutate(ang_logit_z = zscore(ang_logit),
vtc_logit_z = zscore(vtc_logit),
hipp_logit_z = zscore(hipp_logit),
CCN_sig_z = zscore(CCN_sig),
DAN_sig_z = zscore(DAN_sig),
CCN_lh_sig_z = zscore(CCN_lh_sig),
DAN_lh_sig_z = zscore(DAN_lh_sig),
hipp_sig_z = zscore(hipp_sig),
hipp_tail_sig_z = zscore(hipp_tail_sig),
reps = factor(reps),
hipp_quintile = ntile(hipp_sig, 5),
CCN_quintile = ntile(CCN_sig, 5),
DAN_quintile = ntile(DAN_sig, 5),
CCN_lh_quintile = ntile(CCN_lh_sig, 5),
DAN_lh_quintile = ntile(DAN_lh_sig, 5),
hipp_tail_quintile = ntile(hipp_tail_sig, 5),
vtc_quintile = ntile(vtc_logit, 5),
hipp_logit_quintile = ntile(hipp_logit, 5),
ang_quintile = ntile(ang_logit, 5),
rt_z = zscore(respRT))
dCleanStandardized_HC$rGenHipp<-scale(dCleanStandardized_HC$hipp_sig_z)
dCleanStandardized_HC$ERActUnsigned<-scale(dCleanStandardized_HC$vtc_logit_z)
dCleanStandardized_HC$ERActUnsigned_infpar<-scale(dCleanStandardized_HC$ang_logit_z)
dCleanStandardized_HC$RT_z<-scale(dCleanStandardized_HC$rt_z)
contrasts(dCleanStandardized_HC$group) ## [,1]
## control 1
## stress -1contrasts(dCleanStandardized_HC$reps) = c(-1,1); contrasts(dCleanStandardized_HC$reps)## [,1]
## 2 -1
## 4 1contrasts(dCleanStandardized_HC$shockCond) ## [,1]
## safe 1
## threat -1# look at trial counts/subj
trial_counts = data.frame(with(dCleanStandardized_HC, table(subid)))
trial_counts %>% summarise(min(Freq), max(Freq), mean(Freq), sd(Freq))## min(Freq) max(Freq) mean(Freq) sd(Freq)
## 1 8 146 66.59524 34.91387Subj level effects
datalist = list()
i = 1
for (subject in unique(dCleanStandardized_HC$subid)){
print(subject)
fit = lm(vtc_logit_z ~ hipp_sig_z + reps + shockCond + pcorr,
data=dCleanStandardized %>% filter(subid == subject))
print(fit$coefficients['hipp_sig_z'])
dat = data.frame(subid=subject,
coef = fit$coefficients['hipp_sig_z'])
datalist[[i]] <- dat
i = 1+i
}## [1] "ap100"
## hipp_sig_z
## 0.2213994
## [1] "ap101"
## hipp_sig_z
## 0.3373103
## [1] "ap102"
## hipp_sig_z
## 0.5305914
## [1] "ap103"
## hipp_sig_z
## 0.01740553
## [1] "ap104"
## hipp_sig_z
## 0.2433198
## [1] "ap105"
## hipp_sig_z
## 0.3827204
## [1] "ap107"
## hipp_sig_z
## 0.2027479
## [1] "ap108"
## hipp_sig_z
## 0.5068813
## [1] "ap109"
## hipp_sig_z
## 0.03096766
## [1] "ap110"
## hipp_sig_z
## -0.095993
## [1] "ap111"
## hipp_sig_z
## 0.4177546
## [1] "ap113"
## hipp_sig_z
## 0.03273074
## [1] "ap114"
## hipp_sig_z
## -0.007480017
## [1] "ap115"
## hipp_sig_z
## 0.4292727
## [1] "ap116"
## hipp_sig_z
## 0.0864731
## [1] "ap117"
## hipp_sig_z
## 0.1292738
## [1] "ap118"
## hipp_sig_z
## 0.363019
## [1] "ap119"
## hipp_sig_z
## 0.1547364
## [1] "ap120"
## hipp_sig_z
## 0.1606293
## [1] "ap121"
## hipp_sig_z
## -0.06804304
## [1] "ap122"
## hipp_sig_z
## 0.3733573
## [1] "ap158"
## hipp_sig_z
## 0.1679932
## [1] "ap150"
## hipp_sig_z
## 0.07004682
## [1] "ap152"
## hipp_sig_z
## 0.2506595
## [1] "ap153"
## hipp_sig_z
## -0.1482166
## [1] "ap154"
## hipp_sig_z
## 0.5447693
## [1] "ap155"
## hipp_sig_z
## 0.3138483
## [1] "ap157"
## hipp_sig_z
## 0.2869844
## [1] "ap159"
## hipp_sig_z
## 0.1926664
## [1] "ap160"
## hipp_sig_z
## 0.7377234
## [1] "ap161"
## hipp_sig_z
## 0.307614
## [1] "ap162"
## hipp_sig_z
## 0.3733739
## [1] "ap163"
## hipp_sig_z
## 0.3261346
## [1] "ap164"
## hipp_sig_z
## 0.3638596
## [1] "ap165"
## hipp_sig_z
## 0.4880732
## [1] "ap166"
## hipp_sig_z
## 0.1295266
## [1] "ap167"
## hipp_sig_z
## 0.2138185
## [1] "ap169"
## hipp_sig_z
## 0.3528862
## [1] "ap170"
## hipp_sig_z
## 0.5648815
## [1] "ap171"
## hipp_sig_z
## 0.4931997
## [1] "ap172"
## hipp_sig_z
## 0.2994002
## [1] "ap173"
## hipp_sig_z
## 0.3095836slopes_vtcXhipp <- dplyr::bind_rows(datalist) %>%
left_join(dCleanStandardized %>%
group_by(subid, group) %>%
summarise(n()))## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character## Joining, by = "subid"## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vectorwith(slopes_vtcXhipp , boxplot(coef ~ group))
bartlett.test(coef ~ group, data=slopes_vtcXhipp)##
## Bartlett test of homogeneity of variances
##
## data: coef by group
## Bartlett's K-squared = 0.019339, df = 1, p-value = 0.8894t.test(coef ~ group, data=slopes_vtcXhipp, var.equal=TRUE)##
## Two Sample t-test
##
## data: coef by group
## t = -1.947, df = 40, p-value = 0.05858
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.231672992 0.004323095
## sample estimates:
## mean in group control mean in group stress
## 0.2098667 0.3235417ggplot(slopes_vtcXhipp, aes(x=group, y=coef)) +
geom_boxplot(outlier.alpha = 0)+
geom_point(position = position_jitter(width=.1)) +
xlab('') + ylab('Slope (vtc ~ hipp)')
# ## now dont control for pcorr
# datalist = list()
# i = 1
# for (subject in unique(dCleanStandardized_HC$subid)){
# print(subject)
# fit = lm(vtc_logit_z ~ hipp_sig_z + reps + shockCond ,
# data=dCleanStandardized %>% filter(subid == subject))
# print(fit$coefficients['hipp_sig_z'])
# dat = data.frame(subid=subject,
# coef = fit$coefficients['hipp_sig_z'])
# datalist[[i]] <- dat
# i = 1+i
# }
#
# slopes_vtcXhipp <- dplyr::bind_rows(datalist) %>%
# left_join(dCleanStandardized %>%
# group_by(subid, group) %>%
# summarise(n()))
# with(slopes_vtcXhipp , boxplot(coef ~ group))
# bartlett.test(coef ~ group, data=slopes_vtcXhipp)
# t.test(coef ~ group, data=slopes_vtcXhipp, var.equal=TRUE)with(slopes_vtcXhipp %>% left_join(assoc_d %>%
group_by(subid, group) %>%
summarise(assoc_d = mean(assoc_d))),
summary(lm(coef ~ assoc_d)))## Joining, by = c("subid", "group")## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector##
## Call:
## lm(formula = coef ~ assoc_d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.42954 -0.12529 0.01418 0.12295 0.45775
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.29564 0.05885 5.024 1.1e-05 ***
## assoc_d -0.02706 0.04312 -0.628 0.534
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1968 on 40 degrees of freedom
## Multiple R-squared: 0.009753, Adjusted R-squared: -0.015
## F-statistic: 0.394 on 1 and 40 DF, p-value: 0.5338summary(lmer(CCN_lh_sig ~ group + (1|subid),
data=dCleanStandardized_HC %>% filter(CCN_lh_quintile == 1)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: CCN_lh_sig ~ group + (1 | subid)
## Data: dCleanStandardized_HC %>% filter(CCN_lh_quintile == 1)
##
## REML criterion at convergence: -90.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.1887 -0.3880 0.2501 0.6162 1.9068
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.01756 0.1325
## Residual 0.04328 0.2080
## Number of obs: 580, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.29182 0.02279 37.28000 -12.805 3.11e-15 ***
## group1 -0.01607 0.02279 37.28000 -0.705 0.485
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## group1 -0.085summary(lmer(CCN_lh_sig ~ group + (1|subid),
data=dCleanStandardized_HC %>% filter(CCN_lh_quintile == 2)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: CCN_lh_sig ~ group + (1 | subid)
## Data: dCleanStandardized_HC %>% filter(CCN_lh_quintile == 2)
##
## REML criterion at convergence: -1549.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.86384 -0.72655 0.05504 0.74151 2.75467
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.006636 0.08146
## Residual 0.002757 0.05251
## Number of obs: 557, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.001804 0.012872 37.030000 -0.140 0.889
## group1 -0.012634 0.012872 37.030000 -0.982 0.333
##
## Correlation of Fixed Effects:
## (Intr)
## group1 -0.055summary(lmer(CCN_lh_sig ~ group + (1|subid),
data=dCleanStandardized_HC %>% filter(CCN_lh_quintile == 3)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: CCN_lh_sig ~ group + (1 | subid)
## Data: dCleanStandardized_HC %>% filter(CCN_lh_quintile == 3)
##
## REML criterion at convergence: -1662.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.87226 -0.63268 -0.01221 0.70378 2.70905
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.006989 0.08360
## Residual 0.002240 0.04732
## Number of obs: 559, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.14534 0.01317 36.66000 11.039 3.27e-13 ***
## group1 -0.01491 0.01317 36.66000 -1.133 0.265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## group1 -0.056summary(lmer(CCN_lh_sig ~ group + (1|subid),
data=dCleanStandardized_HC %>% filter(CCN_lh_quintile == 4)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: CCN_lh_sig ~ group + (1 | subid)
## Data: dCleanStandardized_HC %>% filter(CCN_lh_quintile == 4)
##
## REML criterion at convergence: -1455.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6040 -0.7250 -0.0121 0.7102 3.7097
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.009020 0.09497
## Residual 0.003237 0.05689
## Number of obs: 557, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.29631 0.01496 38.95000 19.803 <2e-16 ***
## group1 -0.01863 0.01496 38.95000 -1.245 0.221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## group1 -0.054summary(lmer(CCN_lh_sig ~ group + (1|subid),
data=dCleanStandardized_HC %>% filter(CCN_lh_quintile == 5)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: CCN_lh_sig ~ group + (1 | subid)
## Data: dCleanStandardized_HC %>% filter(CCN_lh_quintile == 5)
##
## REML criterion at convergence: -397.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8063 -0.6751 -0.2447 0.5304 4.4087
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.01721 0.1312
## Residual 0.02333 0.1527
## Number of obs: 544, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.55661 0.02178 40.08000 25.558 <2e-16 ***
## group1 -0.04077 0.02178 40.08000 -1.872 0.0685 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## group1 -0.0762b (i): Does hipp still track VTC for HC recollection?
deq.2b<-lmer(ERActUnsigned ~ group*(rGenHipp) + reps + shockCond +
(-1 + rGenHipp|subid)+
(1|subid),
data=dCleanStandardized_HC)
# uncorrelate random slopes and intercepts
summary(deq.2b)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: ERActUnsigned ~ group * (rGenHipp) + reps + shockCond + (-1 +
## rGenHipp | subid) + (1 | subid)
## Data: dCleanStandardized_HC
##
## REML criterion at convergence: 7772.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6305 -0.6618 -0.0434 0.6514 3.6190
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid rGenHipp 0.02952 0.1718
## subid.1 (Intercept) 0.00000 0.0000
## Residual 0.91814 0.9582
## Number of obs: 2797, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.323e-03 1.925e-02 2.749e+03 -0.173 0.8630
## group1 4.574e-04 1.890e-02 2.748e+03 0.024 0.9807
## rGenHipp 2.417e-01 3.358e-02 3.460e+01 7.197 2.27e-08 ***
## reps1 1.610e-02 1.858e-02 2.776e+03 0.867 0.3863
## shockCond1 5.567e-03 1.821e-02 2.772e+03 0.306 0.7598
## group1:rGenHipp -6.581e-02 3.359e-02 3.460e+01 -1.959 0.0582 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp reps1 shckC1
## group1 -0.283
## rGenHipp 0.002 0.000
## reps1 -0.189 0.020 -0.009
## shockCond1 -0.025 0.020 0.003 -0.012
## grp1:rGnHpp 0.003 0.000 -0.126 -0.016 -0.005# look at raw reinstatement by median split of hipp (un-zscored)
dat = dCleanStandardized_HC %>%
filter(pcorr == 1) %>%
group_by(subid, group) %>%
mutate(hipp_med = ntile(hipp_sig, 2)) %>%
ungroup()
summary(lmer(vtc_logit ~ group + reps + shockCond +
(1|subid),
data=dat %>% filter(hipp_med == 1)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: vtc_logit ~ group + reps + shockCond + (1 | subid)
## Data: dat %>% filter(hipp_med == 1)
##
## REML criterion at convergence: 6831.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0915 -0.6310 -0.0181 0.5844 4.3446
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.3039 0.5513
## Residual 7.2466 2.6919
## Number of obs: 1409, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.999e-01 1.177e-01 3.650e+01 2.548 0.0152 *
## group1 2.895e-01 1.167e-01 3.560e+01 2.481 0.0180 *
## reps1 9.286e-02 7.359e-02 1.393e+03 1.262 0.2072
## shockCond1 2.028e-02 7.203e-02 1.382e+03 0.282 0.7783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 reps1
## group1 -0.149
## reps1 -0.132 0.031
## shockCond1 -0.013 0.005 0.026summary(lmer(vtc_logit ~ group + reps + shockCond +
(1|subid),
data=dat %>% filter(hipp_med == 2)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: vtc_logit ~ group + reps + shockCond + (1 | subid)
## Data: dat %>% filter(hipp_med == 2)
##
## REML criterion at convergence: 6725.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6091 -0.6466 -0.0393 0.6514 3.2923
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.6986 0.8358
## Residual 7.1132 2.6670
## Number of obs: 1388, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.38884 0.15403 39.20000 9.016 4.18e-11 ***
## group1 -0.09567 0.15322 38.40000 -0.624 0.536
## reps1 0.04690 0.07404 1362.40000 0.633 0.527
## shockCond1 -0.01133 0.07235 1358.50000 -0.157 0.876
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 reps1
## group1 -0.105
## reps1 -0.103 -0.011
## shockCond1 -0.017 0.025 -0.055# what about rep effect?
summary(lmer(ERActUnsigned ~ group*(rGenHipp) + reps + shockCond +
(-1 + rGenHipp|subid)+
(-1 + reps|subid)+
(1|subid),
data=dCleanStandardized_HC))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: ERActUnsigned ~ group * (rGenHipp) + reps + shockCond + (-1 +
## rGenHipp | subid) + (-1 + reps | subid) + (1 | subid)
## Data: dCleanStandardized_HC
##
## REML criterion at convergence: 7771.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6301 -0.6560 -0.0425 0.6480 3.5980
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid rGenHipp 0.029372 0.17138
## subid.1 reps2 0.003849 0.06204
## reps4 0.001812 0.04257 -1.00
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.915546 0.95684
## Number of obs: 2797, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.555e-03 1.934e-02 1.169e+03 -0.236 0.8138
## group1 1.283e-03 1.890e-02 2.748e+03 0.068 0.9459
## rGenHipp 2.418e-01 3.353e-02 3.460e+01 7.211 2.2e-08 ***
## reps1 1.857e-02 2.064e-02 4.150e+01 0.900 0.3735
## shockCond1 5.727e-03 1.819e-02 2.766e+03 0.315 0.7530
## group1:rGenHipp -6.578e-02 3.353e-02 3.460e+01 -1.962 0.0579 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp reps1 shckC1
## group1 -0.281
## rGenHipp 0.001 0.000
## reps1 -0.212 0.018 -0.007
## shockCond1 -0.025 0.021 0.003 -0.010
## grp1:rGnHpp 0.003 -0.001 -0.126 -0.014 -0.005Hippocampal activity does scale with VTC reinstatement strength across HC recollection; there’s no effect of encoding strength on VTC reinstatement strength when controlling for hippocampal activity. There’s a marginal group interaction, similar to that seen across all old items.
Plot
## collapse across rep
dm_avg = dm %>% filter(pcorr == 1) %>%
group_by(subid) %>%
mutate(hipp_quintile = ntile(hipp_sig, 5)) %>%
group_by(subid, group, hipp_quintile, reps) %>%
summarise(vtc_logit = mean_(vtc_logit), count = n()) %>%
group_by(subid, group, hipp_quintile) %>%
summarise(vtc_logit = mean_(vtc_logit), count = n())
dfwc = summarySEwithin(dm_avg, measurevar="vtc_logit", withinvars=c("hipp_quintile"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95)## Automatically converting the following non-factors to factors: hipp_quintiledfwc## group hipp_quintile N vtc_logit vtc_logit_norm sd se
## 1 control 1 22 0.2010613 0.07894803 1.2933174 0.2757362
## 2 control 2 22 0.8395520 0.71743873 1.0876357 0.2318847
## 3 control 3 22 1.0152743 0.89316103 0.7749401 0.1652178
## 4 control 4 22 1.1257937 1.00368039 0.7405889 0.1578941
## 5 control 5 22 1.6325535 1.51044014 1.0452088 0.2228393
## 6 stress 1 20 -0.3198339 -0.18550922 1.0998454 0.2459329
## 7 stress 2 20 0.1193252 0.25364982 0.7727711 0.1727969
## 8 stress 3 20 0.8028162 0.93714080 1.1203010 0.2505069
## 9 stress 4 20 1.0758159 1.21014052 0.9566780 0.2139197
## 10 stress 5 20 1.8539218 1.98824641 1.4295128 0.3196488
## ci
## 1 0.5734248
## 2 0.4822307
## 3 0.3435892
## 4 0.3283587
## 5 0.4634196
## 6 0.5147435
## 7 0.3616680
## 8 0.5243170
## 9 0.4477391
## 10 0.6690326p2 = ggplot(dfwc, aes(x=hipp_quintile, y=vtc_logit, group=group, color=group)) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=vtc_logit-se,
ymax=vtc_logit+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('Reinstatement strength\n(logits)')+
xlab('Hippocampal activity (quintiles)')+
scale_color_manual(values=palette, guide = guide_legend(title = NULL))
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/logitXhipp_quintile_HCrecollection_wholehipp.jpg',
dpi=150, width = 6, height=4)
p2
2b (ii): Is frontoparietal activity modulated by RT/difficulty of HC decisions?
Predict that more uncertain HC recollection decisions should scale with RT + frontoparietal bold (particularly in LH). We’ll test lh first, then look to see if holds in bilateral network.
Since encoding strength tracks RT for HC decisions (faster RTs for stronger encoding items), these might require less FP activity to recall.
Would hipp be the reverse? Greater activity for more vivid/confident/faster memories? Or, would hipp be greater for slower HC recollection of associate, where potentially have retrieved more things/activated more neurons in hipp before settling on correct response?
Include all subjects included in fMRI analyses:
d = read.csv('/Users/sgagnon/Dropbox/Stanford/Presentations/AP/mvpa_logit_place_byreps_avg_46810_filtartloc_scalewithinrun.csv')
# filter d to just good subjs, and only OLD items
d = d %>%
filter(cond %in% c('sourcemiss_hi', 'sourcehit', 'M', 'itemhit_lo')) %>%
mutate(pcorr = factor(ifelse(cond == "sourcehit", 1, 0)),
vtc_logit = avg_logit)
dt %>% filter(shock_and_post == 2) # one subj w/consecutive shocks - trial needs to be removed (though mem_condition == 'nuisance')## X run trial onset duration shockCond shockTrial target associate
## 1 6677 5 13 141.3117 11.5306 threat 1 WHIP ocean
## resp acc accSpec respRT subid group remove anxious happy safe
## 1 outdoor H HO_Hi 0.1927 ap160 stress-fmri NA 4 3 6
## stressed life_stress sleep cond_orig cond reps shock_and_post conf
## 1 5 3 4.5 TO_4 outdoor 4 2 Hi
## onset_adj mem_conditions obj_conditions onset_code rt_z
## 1 129.3117 nuisance nuisance nuisance -3.136573
## mem_conditions_byrep onset_code_byrep
## 1 nuisance-4 nuisance-4# Merge w/other behavioral info
d = dt %>%
mutate(onset=onset_adj, img_type=cond) %>%
dplyr::select(-group, -reps, -cond) %>%
right_join(d, by=c('subid', 'run', 'onset')) %>%
mutate(subid = factor(subid), imgType = factor(img_type))## Warning in right_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vectordim(d)## [1] 6942 50d = d %>% filter(shock_and_post == 0)
dim(d)## [1] 6941 50# Read in hipp BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-hippocampus.csv')
roi_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-hippocampus.csv')
# dim(roi_lh); dim(roi_rh)
# Take timepoints of interest for old trials, and collapse across hemispheres
roi_f = bind_rows("lh" = roi_lh, "rh" = roi_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, hemi, run, onset) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset) %>%
summarise(hipp_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character# Read in hipp tail BOLD
roi_tail_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-hippocampus-tail.csv')
roi_tail_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-hippocampus-tail.csv')
# Take timepoints of interest for old trials, and collapse across hemispheres
roi_tail_f = bind_rows("lh" = roi_tail_lh, "rh" = roi_tail_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, hemi, run, onset) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset) %>%
summarise(hipp_tail_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character# Read in angular BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-DefaultA_IPL.csv')
angular_lh_f = roi_lh %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, run, onset) %>%
summarise(angular_lh_sig = mean_(mean_activity)) %>% ungroup()
# Read in RSP BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-DefaultC_Rsp.csv')
roi_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-DefaultC_Rsp.csv')
# Take timepoints of interest for old trials, and collapse across hemispheres
rsp_f = bind_rows("lh" = roi_lh, "rh" = roi_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, hemi, run, onset) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset) %>%
summarise(rsp_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character# Read in CCN BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-frontoparietal.csv')
roi_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-frontoparietal.csv')
# dim(roi_lh); dim(roi_rh)
# Take timepoints of interest for old trials, and collapse across hemispheres
CCN_f = bind_rows("lh" = roi_lh, "rh" = roi_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, hemi, run, onset) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset) %>%
summarise(CCN_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character# also, just for LH
CCN_lh_f = roi_lh %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, run, onset) %>%
summarise(CCN_lh_sig = mean_(mean_activity)) %>% ungroup()
# Read in DAN BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-dorsalattn.csv')
roi_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-dorsalattn.csv')
# dim(roi_lh); dim(roi_rh)
# Take timepoints of interest for old trials, and collapse across hemispheres
DAN_f = bind_rows("lh" = roi_lh, "rh" = roi_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, hemi, run, onset) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset) %>%
summarise(DAN_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character# also, just for LH
DAN_lh_f = roi_lh %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, run, onset) %>%
summarise(DAN_lh_sig = mean_(mean_activity)) %>% ungroup()
# Other reinstatement
d_infpar = read.csv('/Users/sgagnon/Dropbox/Stanford/Presentations/AP/mvpa_logit_inferiorparietal_place_byreps_avg_46810_filtartloc_scalewithinrun.csv') %>%
filter(cond %in% c('sourcemiss_hi', 'sourcehit', 'M', 'itemhit_lo')) %>%
mutate(ang_logit = avg_logit) %>%
dplyr::select(subid, run, onset, ang_logit)
d_hipp = read.csv('/Users/sgagnon/Dropbox/Stanford/Presentations/AP/mvpa_logit_bilat-hippocampus_place_byreps_avg_46810_filtartloc_scalewithinrun.csv') %>%
filter(cond %in% c('sourcemiss_hi', 'sourcehit', 'M', 'itemhit_lo')) %>%
mutate(pcorr = factor(ifelse(cond == "sourcehit", 1, 0)),
hipp_logit = avg_logit) %>%
dplyr::select(subid, run, onset, hipp_logit)
# Merge w/VTC classifier evidence
dm_allfmri = d %>%
left_join(roi_f, by=c('subid', 'run', 'onset')) %>%
left_join(roi_tail_f, by=c('subid', 'run', 'onset')) %>%
left_join(DAN_f, by=c('subid', 'run', 'onset')) %>%
left_join(CCN_f, by=c('subid', 'run', 'onset')) %>%
left_join(rsp_f, by=c('subid', 'run', 'onset')) %>%
left_join(angular_lh_f, by=c('subid', 'run', 'onset')) %>%
left_join(DAN_lh_f, by=c('subid', 'run', 'onset')) %>%
left_join(CCN_lh_f, by=c('subid', 'run', 'onset')) %>%
left_join(d_infpar, by=c('subid', 'run', 'onset')) %>%
left_join(d_hipp, by=c('subid', 'run', 'onset')) %>%
group_by(subid) %>%
mutate(ang_logit_z = zscore(ang_logit),
vtc_logit_z = zscore(vtc_logit),
hipp_logit_z = zscore(hipp_logit),
CCN_sig_z = zscore(CCN_sig),
rsp_sig_z = zscore(rsp_sig),
DAN_sig_z = zscore(DAN_sig),
angular_lh_sig_z = zscore(angular_lh_sig),
CCN_lh_sig_z = zscore(CCN_lh_sig),
DAN_lh_sig_z = zscore(DAN_lh_sig),
hipp_sig_z = zscore(hipp_sig),
hipp_tail_sig_z = zscore(hipp_tail_sig),
reps = factor(reps),
hipp_quintile = ntile(hipp_sig, 5),
rsp_quintile = ntile(rsp_sig, 5),
CCN_quintile = ntile(CCN_sig, 5),
DAN_quintile = ntile(DAN_sig, 5),
angular_lh_sig_quintile = ntile(angular_lh_sig, 5),
CCN_lh_quintile = ntile(CCN_lh_sig, 5),
DAN_lh_quintile = ntile(DAN_lh_sig, 5),
hipp_tail_quintile = ntile(hipp_tail_sig, 5),
vtc_quintile = ntile(vtc_logit, 5),
hipp_logit_quintile = ntile(hipp_logit, 5),
ang_quintile = ntile(ang_logit, 5)) %>%
ungroup() %>%
mutate(subid = factor(subid),
cond = factor(cond),
condition = factor(condition))
dim(dm_allfmri)## [1] 6941 82with(dm_allfmri, table(subid, reps))## reps
## subid 2 4
## ap100 76 78
## ap101 83 82
## ap102 80 79
## ap103 83 79
## ap104 82 83
## ap105 81 81
## ap107 79 82
## ap108 72 74
## ap109 81 77
## ap110 82 81
## ap111 82 84
## ap113 83 83
## ap114 81 81
## ap115 78 82
## ap116 80 82
## ap117 84 80
## ap118 83 81
## ap119 80 81
## ap120 79 76
## ap121 84 81
## ap122 84 82
## ap150 77 78
## ap152 73 78
## ap153 77 75
## ap154 67 78
## ap155 69 66
## ap157 77 79
## ap158 84 83
## ap159 81 81
## ap160 66 64
## ap161 81 79
## ap162 75 80
## ap163 83 78
## ap164 79 79
## ap165 75 81
## ap166 79 79
## ap167 76 77
## ap168 79 82
## ap169 77 79
## ap170 81 81
## ap171 81 81
## ap172 80 76
## ap173 73 74
## ap174 77 80str(dm_allfmri)## Classes 'tbl_df', 'tbl' and 'data.frame': 6941 obs. of 82 variables:
## $ X.x : int 0 2 3 4 5 7 8 11 14 15 ...
## $ run : num 1 1 1 1 1 1 1 1 1 1 ...
## $ trial : int 1 3 4 5 6 8 9 12 15 16 ...
## $ onset : num 0.0191 19.8642 29.3132 40.6786 50.4701 ...
## $ duration : num 10.67 9.42 11.34 9.76 9.12 ...
## $ shockCond : Factor w/ 2 levels "safe","threat": 1 1 1 1 1 1 1 1 1 1 ...
## $ shockTrial : int 0 0 0 0 0 0 0 0 0 0 ...
## $ target : Factor w/ 252 levels "ACCORDIAN","ACROBAT",..: 15 238 116 114 183 224 87 102 192 191 ...
## $ associate : Factor w/ 241 levels "\002\002","\002",..: 68 237 126 61 175 233 53 118 173 84 ...
## $ resp : Factor w/ 4 levels "foil","indoor",..: 2 2 4 4 4 1 1 1 4 2 ...
## $ acc : Factor w/ 6 levels "CR","FA","H",..: 6 3 3 3 3 4 4 4 3 3 ...
## $ accSpec : Factor w/ 15 levels "CR","FAI_Hi",..: 14 6 8 8 8 10 10 10 8 6 ...
## $ respRT : num 2.11 1.68 1.73 1.37 2.15 ...
## $ subid : Factor w/ 44 levels "ap100","ap101",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ remove : logi NA NA NA NA NA NA ...
## $ anxious : int 3 3 3 3 3 3 3 3 3 3 ...
## $ happy : int 2 2 2 2 2 2 2 2 2 2 ...
## $ safe : int 3 3 3 3 3 3 3 3 3 3 ...
## $ stressed : int 3 3 3 3 3 3 3 3 3 3 ...
## $ life_stress : int 4 4 4 4 4 4 4 4 4 4 ...
## $ sleep : num 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 ...
## $ cond_orig : Factor w/ 5 levels "F_0","TI_2","TI_4",..: 5 2 4 4 5 3 2 4 5 2 ...
## $ shock_and_post : int 0 0 0 0 0 0 0 0 0 0 ...
## $ conf : Factor w/ 3 levels "Hi","Lo","N": 1 1 1 1 1 3 3 3 1 1 ...
## $ onset_adj : num 0.0191 19.8642 29.3132 40.6786 50.4701 ...
## $ mem_conditions : Factor w/ 7 levels "CR","FA","itemhit_lo",..: 7 6 6 6 6 4 4 4 6 6 ...
## $ obj_conditions : Factor w/ 3 levels "new","nuisance",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ onset_code : Factor w/ 6 levels "CR","FA","itemhit",..: 3 6 6 6 6 4 4 4 6 6 ...
## $ rt_z : num -1.2039 -0.746 -0.6739 -1.2235 -0.0194 ...
## $ mem_conditions_byrep : Factor w/ 13 levels "CR-0","FA-0",..: 13 10 10 10 11 6 5 5 11 10 ...
## $ onset_code_byrep : Factor w/ 11 levels "CR-0","FA-0",..: 4 10 10 10 11 6 5 5 11 10 ...
## $ img_type : Factor w/ 3 levels "foil","indoor",..: 3 2 3 3 3 2 2 3 3 2 ...
## $ X.y : int 2 8 11 14 17 23 26 35 44 47 ...
## $ index : int 2 8 11 14 17 23 26 35 44 47 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## $ condition : Factor w/ 8 levels "itemhit_lo-2",..: 8 5 5 5 6 4 3 3 6 5 ...
## $ category : Factor w/ 1 level "place": 1 1 1 1 1 1 1 1 1 1 ...
## $ X0 : num -6.635 -9.265 6.804 8.431 0.203 ...
## $ X2 : num -6.32 -4.39 7.22 3.92 -3.54 ...
## $ X4 : num -1.49 5.79 4.22 1.8 -2.75 ...
## $ X6 : num 2.59 5.69 -3.03 3.7 3.74 ...
## $ X8 : num -1.03 8.65 -1.68 -1.28 -2.28 ...
## $ X10 : num -0.642 6.486 8.468 -0.23 -9.405 ...
## $ X12 : num -1.1 6.64 11.49 -3.55 -5.64 ...
## $ avg_logit : num -0.145 6.655 1.996 0.998 -2.673 ...
## $ cond : Factor w/ 4 levels "itemhit_lo","M",..: 4 3 3 3 3 2 2 2 3 3 ...
## $ reps : Factor w/ 2 levels "2","4": 2 1 1 1 2 2 1 1 2 1 ...
## $ pcorr : Factor w/ 2 levels "0","1": 1 2 2 2 2 1 1 1 2 2 ...
## $ vtc_logit : num -0.145 6.655 1.996 0.998 -2.673 ...
## $ imgType : Factor w/ 2 levels "indoor","outdoor": 2 1 2 2 2 1 1 2 2 1 ...
## $ hipp_sig : num 0.2754 0.4613 -0.1955 -0.0154 -0.14 ...
## $ hipp_tail_sig : num 0.11449 0.49252 -0.00556 -0.06895 -0.13196 ...
## $ DAN_sig : num 0.175 0.658 0.225 -0.262 -0.395 ...
## $ CCN_sig : num 0.367 0.4107 0.0192 -0.1273 -0.2068 ...
## $ rsp_sig : num 0.3053 1.5956 -0.0991 -0.19 -0.5123 ...
## $ angular_lh_sig : num 0.20058 0.85916 -0.09532 0.00574 -0.04293 ...
## $ DAN_lh_sig : num 0.212 0.585 0.183 -0.233 -0.383 ...
## $ CCN_lh_sig : num 0.3687 0.2566 0.1633 -0.1271 -0.0121 ...
## $ ang_logit : num -2.066 5.935 0.249 -3.792 -6.49 ...
## $ hipp_logit : num -2.553 1.418 -1.783 -2.571 -0.555 ...
## $ ang_logit_z : num -0.474 1.931 0.222 -0.993 -1.804 ...
## $ vtc_logit_z : num -0.148 1.895 0.495 0.195 -0.907 ...
## $ hipp_logit_z : num -0.491 1.406 -0.123 -0.499 0.464 ...
## $ CCN_sig_z : num 0.6436 0.7336 -0.0722 -0.3738 -0.5374 ...
## $ rsp_sig_z : num 0.209 2.071 -0.375 -0.506 -0.971 ...
## $ DAN_sig_z : num 0.391 1.606 0.516 -0.711 -1.046 ...
## $ angular_lh_sig_z : num 0.177 1.513 -0.423 -0.218 -0.317 ...
## $ CCN_lh_sig_z : num 0.537 0.31 0.121 -0.468 -0.235 ...
## $ DAN_lh_sig_z : num 0.437 1.389 0.363 -0.7 -1.082 ...
## $ hipp_sig_z : num 0.537 0.967 -0.552 -0.135 -0.424 ...
## $ hipp_tail_sig_z : num 0.167 1.146 -0.143 -0.307 -0.47 ...
## $ hipp_quintile : int 4 5 2 3 2 1 4 1 3 4 ...
## $ rsp_quintile : int 3 5 2 2 1 1 4 1 3 3 ...
## $ CCN_quintile : int 4 4 3 2 2 1 5 1 3 3 ...
## $ DAN_quintile : int 4 5 4 2 1 1 4 1 2 4 ...
## $ angular_lh_sig_quintile: int 3 5 2 3 2 1 2 1 4 4 ...
## $ CCN_lh_quintile : int 4 3 3 2 3 1 5 1 4 3 ...
## $ DAN_lh_quintile : int 4 5 4 2 1 1 4 1 3 4 ...
## $ hipp_tail_quintile : int 3 5 3 2 2 1 4 1 3 5 ...
## $ vtc_quintile : int 3 5 4 3 1 2 2 1 5 4 ...
## $ hipp_logit_quintile : int 2 5 3 2 4 2 5 3 1 3 ...
## $ ang_quintile : int 2 5 4 1 1 1 4 2 4 5 ...# set up contrasts
contrasts(dm_allfmri$pcorr) = c(-1,1); contrasts(dm_allfmri$pcorr)## [,1]
## 0 -1
## 1 1contrasts(dm_allfmri$reps) = c(-1,1); contrasts(dm_allfmri$reps)## [,1]
## 2 -1
## 4 1contrasts(dm_allfmri$group) = c(1,-1); contrasts(dm_allfmri$group)## [,1]
## control 1
## stress -1contrasts(dm_allfmri$shockCond) = c(1,-1); contrasts(dm_allfmri$shockCond)## [,1]
## safe 1
## threat -1# standardize continuous vars
dCleanStandardized_HC_allfmri = dm_allfmri %>% filter(pcorr == 1)
with(dCleanStandardized_HC_allfmri, table(subid, reps))## reps
## subid 2 4
## ap100 38 49
## ap101 37 65
## ap102 23 43
## ap103 48 63
## ap104 67 79
## ap105 49 68
## ap107 60 62
## ap108 12 16
## ap109 20 38
## ap110 3 17
## ap111 23 50
## ap113 28 47
## ap114 43 58
## ap115 32 45
## ap116 69 75
## ap117 36 48
## ap118 21 41
## ap119 44 68
## ap120 29 42
## ap121 16 17
## ap122 40 55
## ap150 28 38
## ap152 15 25
## ap153 7 24
## ap154 14 28
## ap155 18 20
## ap157 13 38
## ap158 2 9
## ap159 21 29
## ap160 23 28
## ap161 9 18
## ap162 19 22
## ap163 48 59
## ap164 28 53
## ap165 28 36
## ap166 27 44
## ap167 14 24
## ap168 18 28
## ap169 33 36
## ap170 27 47
## ap171 9 24
## ap172 9 11
## ap173 2 6
## ap174 35 49dCleanStandardized_HC_allfmri = dCleanStandardized_HC_allfmri %>%
group_by(subid) %>%
mutate(ang_logit_z = zscore(ang_logit),
vtc_logit_z = zscore(vtc_logit),
hipp_logit_z = zscore(hipp_logit),
CCN_sig_z = zscore(CCN_sig),
DAN_sig_z = zscore(DAN_sig),
CCN_lh_sig_z = zscore(CCN_lh_sig),
DAN_lh_sig_z = zscore(DAN_lh_sig),
hipp_sig_z = zscore(hipp_sig),
hipp_tail_sig_z = zscore(hipp_tail_sig),
reps = factor(reps),
hipp_quintile = ntile(hipp_sig, 5),
CCN_quintile = ntile(CCN_sig, 5),
DAN_quintile = ntile(DAN_sig, 5),
CCN_lh_quintile = ntile(CCN_lh_sig, 5),
DAN_lh_quintile = ntile(DAN_lh_sig, 5),
hipp_tail_quintile = ntile(hipp_tail_sig, 5),
vtc_quintile = ntile(vtc_logit, 5),
hipp_logit_quintile = ntile(hipp_logit, 5),
ang_quintile = ntile(ang_logit, 5),
rt_z = zscore(respRT)) %>%
ungroup()
dCleanStandardized_HC_allfmri$rGenHipp<-scale(dCleanStandardized_HC_allfmri$hipp_sig_z)
dCleanStandardized_HC_allfmri$ERActUnsigned<-scale(dCleanStandardized_HC_allfmri$vtc_logit_z)
dCleanStandardized_HC_allfmri$ERActUnsigned_infpar<-scale(dCleanStandardized_HC_allfmri$ang_logit_z)
dCleanStandardized_HC_allfmri$RT_z<-scale(dCleanStandardized_HC_allfmri$rt_z)
contrasts(dCleanStandardized_HC_allfmri$group) ## [,1]
## control 1
## stress -1contrasts(dCleanStandardized_HC_allfmri$reps) = c(-1,1); contrasts(dCleanStandardized_HC_allfmri$reps)## [,1]
## 2 -1
## 4 1contrasts(dCleanStandardized_HC_allfmri$shockCond) ## [,1]
## safe 1
## threat -1Modulation of HC recollection decisions by RT
LH CCN
mean(dCleanStandardized_HC_allfmri$RT_z)## [1] -1.242399e-17sd(dCleanStandardized_HC_allfmri$RT_z)## [1] 1mean(dCleanStandardized_HC_allfmri %>% filter(subid =='ap150') %>% pull(RT_z))## [1] 1.233477e-16sd(dCleanStandardized_HC_allfmri %>% filter(subid =='ap150') %>% pull(RT_z))## [1] 1.00743with(dCleanStandardized_HC_allfmri, table(subid))## subid
## ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ap110 ap111 ap113
## 87 102 66 111 146 117 122 28 58 20 73 75
## ap114 ap115 ap116 ap117 ap118 ap119 ap120 ap121 ap122 ap150 ap152 ap153
## 101 77 144 84 62 112 71 33 95 66 40 31
## ap154 ap155 ap157 ap158 ap159 ap160 ap161 ap162 ap163 ap164 ap165 ap166
## 42 38 51 11 50 51 27 41 107 81 64 71
## ap167 ap168 ap169 ap170 ap171 ap172 ap173 ap174
## 38 46 69 74 33 20 8 84dCleanStandardized_HC_allfmri %>% group_by(subid, group) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 22
## 2 stress 22# first lh only
fit = lmer(scale(CCN_lh_sig_z) ~ group * (RT_z + reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri)
summary(fit)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(CCN_lh_sig_z) ~ group * (RT_z + reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC_allfmri
##
## REML criterion at convergence: 8277.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8445 -0.6218 0.0195 0.6429 3.7469
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 9.075e-15 9.526e-08
## reps4 7.169e-15 8.467e-08 -1.00
## subid.1 RT_z 2.979e-03 5.458e-02
## subid.2 (Intercept) 0.000e+00 0.000e+00
## Residual 9.756e-01 9.877e-01
## Number of obs: 2927, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.040e-03 1.914e-02 2.884e+03 -0.211 0.8329
## group1 -7.167e-03 1.914e-02 2.884e+03 -0.374 0.7081
## RT_z 1.275e-01 2.090e-02 3.610e+01 6.098 5.08e-07 ***
## reps1 2.332e-02 1.931e-02 2.915e+03 1.208 0.2273
## shockCond1 1.368e-02 1.829e-02 2.919e+03 0.748 0.4546
## group1:RT_z 5.394e-02 2.090e-02 3.610e+01 2.581 0.0141 *
## group1:reps1 3.998e-02 1.931e-02 2.915e+03 2.071 0.0385 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 g1:RT_
## group1 -0.234
## RT_z -0.021 0.004
## reps1 -0.200 0.064 0.106
## shockCond1 -0.016 0.014 -0.012 -0.015
## group1:RT_z 0.004 -0.021 -0.185 -0.010 0.011
## group1:rps1 0.064 -0.201 -0.009 -0.228 -0.014 0.106#make sure holds when removing rep effect
# summary(lmer(scale(CCN_lh_sig_z) ~ group * (RT_z + reps) + shockCond +
# (-1 + RT_z|subid)+
# (1 |subid),
# data=dCleanStandardized_HC_allfmri))
# explore interaction of group * rt
summary(lmer(scale(CCN_lh_sig_z) ~ RT_z + reps + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri%>% filter(group == 'control')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(CCN_lh_sig_z) ~ RT_z + reps + shockCond + (-1 + reps |
## subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC_allfmri %>% filter(group == "control")
##
## REML criterion at convergence: 5052.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8479 -0.6044 0.0358 0.6425 3.7712
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.000e+00 0.000e+00
## reps4 8.220e-15 9.066e-08 NaN
## subid.1 RT_z 2.648e-03 5.146e-02
## subid.2 (Intercept) 0.000e+00 0.000e+00
## Residual 9.649e-01 9.823e-01
## Number of obs: 1795, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.116e-02 2.356e-02 1.772e+03 -0.474 0.63579
## RT_z 1.813e-01 2.624e-02 1.630e+01 6.909 3.16e-06 ***
## reps1 6.339e-02 2.386e-02 1.782e+03 2.657 0.00795 **
## shockCond1 4.924e-03 2.322e-02 1.789e+03 0.212 0.83207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1
## RT_z -0.022
## reps1 -0.177 0.123
## shockCond1 -0.002 -0.001 -0.030summary(lmer(scale(CCN_lh_sig_z) ~ RT_z + reps + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri %>% filter(group == 'stress'),
control=lmerControl(optimizer="Nelder_Mead",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(CCN_lh_sig_z) ~ RT_z + reps + shockCond + (-1 + reps |
## subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC_allfmri %>% filter(group == "stress")
## Control:
## lmerControl(optimizer = "Nelder_Mead", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 3223.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7767 -0.6509 0.0018 0.6528 3.1614
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.004215 0.06492
## reps4 0.001724 0.04152 -1.00
## subid.1 RT_z 0.003693 0.06077
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.989763 0.99487
## Number of obs: 1132, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.546e-03 3.047e-02 4.557e+02 0.084 0.9334
## RT_z 7.327e-02 3.300e-02 1.690e+01 2.220 0.0404 *
## reps1 -1.663e-02 3.292e-02 1.610e+01 -0.505 0.6204
## shockCond1 2.774e-02 2.968e-02 1.127e+03 0.935 0.3501
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1
## RT_z -0.020
## reps1 -0.235 0.088
## shockCond1 -0.032 -0.023 0.003# check to make sure simple effects are same
# contrasts(dCleanStandardized_HC_allfmri$group) = c(0,1)
# summary(lmer(scale(CCN_lh_sig_z) ~ group * (RT_z + reps) + shockCond +
# (-1 + reps|subid)+
# (-1 + RT_z|subid)+
# (1 |subid),
# data=dCleanStandardized_HC_allfmri))
# contrasts(dCleanStandardized_HC_allfmri$group) = c(1,0)
# summary(lmer(scale(CCN_lh_sig_z) ~ group * (RT_z + reps) + shockCond +
# (-1 + reps|subid)+
# (-1 + RT_z|subid)+
# (1 |subid),
# data=dCleanStandardized_HC_allfmri))
# contrasts(dCleanStandardized_HC_allfmri$group) = c(1,-1)
# interaction between shockcond and RT on CCN? in stress group
# remove subjs w/trial counts/bin < 6
dCleanStandardized_HC_allfmri %>% filter(group == 'stress') %>%
group_by(subid, shockCond) %>%
summarise(count=n()) %>%
ungroup() %>%
mutate(subid = factor(subid)) %>%
complete(subid, shockCond, fill = list(count= 0)) %>% filter(count < 6)## # A tibble: 1 x 3
## subid shockCond count
## <fctr> <fctr> <dbl>
## 1 ap173 threat 1summary(lmer(scale(CCN_lh_sig_z) ~ (RT_z + reps) + shockCond +
shockCond:RT_z +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(-1 + shockCond|subid)+
(-1 + shockCond:RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri %>%
filter(group == 'stress', subid != 'ap173')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## scale(CCN_lh_sig_z) ~ (RT_z + reps) + shockCond + shockCond:RT_z +
## (-1 + reps | subid) + (-1 + RT_z | subid) + (-1 + shockCond |
## subid) + (-1 + shockCond:RT_z | subid) + (1 | subid)
## Data:
## dCleanStandardized_HC_allfmri %>% filter(group == "stress", subid !=
## "ap173")
##
## REML criterion at convergence: 3200.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8259 -0.6616 0.0117 0.6450 3.2091
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 9.012e-04 3.002e-02
## reps4 3.752e-04 1.937e-02 -1.00
## subid.1 RT_z 0.000e+00 0.000e+00
## subid.2 shockCondsafe 0.000e+00 0.000e+00
## shockCondthreat 1.999e-15 4.471e-08 NaN
## subid.3 shockCondsafe:RT_z 2.812e-02 1.677e-01
## shockCondthreat:RT_z 1.057e-03 3.252e-02 -1.00
## subid.4 (Intercept) 0.000e+00 0.000e+00
## Residual 9.806e-01 9.903e-01
## Number of obs: 1124, groups: subid, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.779e-03 3.039e-02 7.406e+02 0.124 0.9011
## RT_z 7.902e-02 3.370e-02 3.510e+01 2.345 0.0248 *
## reps1 -1.937e-02 3.111e-02 1.450e+01 -0.623 0.5432
## shockCond1 2.945e-02 2.960e-02 1.103e+03 0.995 0.3201
## RT_z:shockCond1 2.647e-02 3.740e-02 2.250e+01 0.708 0.4864
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1 shckC1
## RT_z -0.015
## reps1 -0.218 0.085
## shockCond1 -0.022 -0.018 0.001
## RT_z:shckC1 -0.022 0.283 0.027 0.001dCleanStandardized_HC_allfmri %>% filter(group == 'stress',
subid != 'ap173') %>%
group_by(subid, group) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 1 x 2
## group `n()`
## <fctr> <int>
## 1 stress 21# is the RT * rep interaction significant?
dCleanStandardized_HC_allfmri %>%
group_by(subid, reps) %>%
summarise(count=n()) %>%
ungroup() %>%
mutate(subid = factor(subid)) %>%
complete(subid, reps, fill = list(count= 0)) %>% filter(count < 6)## # A tibble: 3 x 3
## subid reps count
## <fctr> <fctr> <dbl>
## 1 ap110 2 3
## 2 ap158 2 2
## 3 ap173 2 2summary(lmer(scale(CCN_lh_sig_z) ~ group * (RT_z * reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(-1 + RT_z:reps|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri %>%
filter(!subid %in% c('ap110', 'ap158', 'ap173'))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(CCN_lh_sig_z) ~ group * (RT_z * reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (-1 + RT_z:reps | subid) +
## (1 | subid)
## Data: dCleanStandardized_HC_allfmri %>% filter(!subid %in% c("ap110",
## "ap158", "ap173"))
##
## REML criterion at convergence: 8182.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8362 -0.6218 0.0163 0.6481 3.7456
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.000e+00 0.000e+00
## reps4 7.339e-18 2.709e-09 NaN
## subid.1 RT_z 0.000e+00 0.000e+00
## subid.2 RT_z:reps2 3.288e-03 5.734e-02
## RT_z:reps4 2.667e-03 5.164e-02 1.00
## subid.3 (Intercept) 0.000e+00 0.000e+00
## Residual 9.772e-01 9.885e-01
## Number of obs: 2888, groups: subid, 41
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.818e-03 1.940e-02 2.844e+03 -0.197 0.8440
## group1 -8.760e-03 1.939e-02 2.844e+03 -0.452 0.6515
## RT_z 1.256e-01 2.121e-02 3.420e+01 5.925 1.05e-06 ***
## reps1 2.135e-02 1.943e-02 2.874e+03 1.099 0.2720
## shockCond1 1.368e-02 1.843e-02 2.878e+03 0.742 0.4581
## RT_z:reps1 -8.490e-05 1.927e-02 1.707e+03 -0.004 0.9965
## group1:RT_z 5.170e-02 2.121e-02 3.420e+01 2.438 0.0201 *
## group1:reps1 3.917e-02 1.943e-02 2.874e+03 2.016 0.0439 *
## group1:RT_z:reps1 -1.566e-02 1.927e-02 1.705e+03 -0.813 0.4164
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 RT_z:1 gr1:RT_ grp1:1
## group1 -0.225
## RT_z -0.034 0.006
## reps1 -0.199 0.065 0.110
## shockCond1 -0.014 0.008 -0.011 -0.013
## RT_z:reps1 0.122 -0.007 -0.131 -0.035 0.011
## group1:RT_z 0.006 -0.034 -0.183 -0.008 0.014 0.042
## group1:rps1 0.065 -0.199 -0.008 -0.224 -0.013 0.007 0.110
## grp1:RT_z:1 -0.006 0.122 0.042 0.008 -0.020 -0.223 -0.131 -0.034dCleanStandardized_HC_allfmri %>% filter(!subid %in% c('ap110', 'ap158', 'ap173')) %>%
group_by(subid, group) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 20
## 2 stress 21LH DAN
### DAN
summary(lmer(scale(DAN_lh_sig_z) ~ group * (RT_z + reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(DAN_lh_sig_z) ~ group * (RT_z + reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC_allfmri
##
## REML criterion at convergence: 8285.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1948 -0.5974 -0.0029 0.6517 3.8551
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.002656 0.05153
## reps4 0.001202 0.03467 -1.00
## subid.1 RT_z 0.002606 0.05105
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.976949 0.98841
## Number of obs: 2927, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.049e-03 1.923e-02 1.348e+03 -0.315 0.7531
## group1 -3.567e-03 1.923e-02 1.347e+03 -0.186 0.8528
## RT_z 1.205e-01 2.070e-02 4.100e+01 5.822 7.75e-07 ***
## reps1 3.215e-02 2.058e-02 4.010e+01 1.562 0.1262
## shockCond1 -6.863e-03 1.831e-02 2.913e+03 -0.375 0.7078
## group1:RT_z 5.567e-02 2.070e-02 4.100e+01 2.690 0.0103 *
## group1:reps1 2.110e-02 2.058e-02 4.010e+01 1.025 0.3115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 g1:RT_
## group1 -0.232
## RT_z -0.021 0.004
## reps1 -0.215 0.060 0.100
## shockCond1 -0.016 0.015 -0.012 -0.014
## group1:RT_z 0.004 -0.021 -0.189 -0.010 0.011
## group1:rps1 0.060 -0.216 -0.010 -0.202 -0.015 0.100# explore interaction of group * rt
summary(lmer(scale(DAN_lh_sig_z) ~ RT_z + reps + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri%>% filter(group == 'control')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(DAN_lh_sig_z) ~ RT_z + reps + shockCond + (-1 + reps |
## subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC_allfmri %>% filter(group == "control")
##
## REML criterion at convergence: 5058.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8479 -0.5884 0.0218 0.6535 3.8557
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.000e+00 0.000e+00
## reps4 7.296e-16 2.701e-08 NaN
## subid.1 RT_z 1.630e-03 4.037e-02
## subid.2 (Intercept) 0.000e+00 0.000e+00
## Residual 9.687e-01 9.842e-01
## Number of obs: 1795, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -9.165e-03 2.361e-02 1.778e+03 -0.388 0.6979
## RT_z 1.765e-01 2.524e-02 2.270e+01 6.995 4.28e-07 ***
## reps1 5.189e-02 2.388e-02 1.782e+03 2.173 0.0299 *
## shockCond1 8.016e-03 2.326e-02 1.790e+03 0.345 0.7304
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1
## RT_z -0.023
## reps1 -0.177 0.129
## shockCond1 -0.002 -0.001 -0.030summary(lmer(scale(DAN_lh_sig_z) ~ RT_z + reps + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri%>% filter(group == 'stress'),
control=lmerControl(optimizer="Nelder_Mead",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(DAN_lh_sig_z) ~ RT_z + reps + shockCond + (-1 + reps |
## subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC_allfmri %>% filter(group == "stress")
## Control:
## lmerControl(optimizer = "Nelder_Mead", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 3223.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1470 -0.6047 -0.0237 0.6605 3.3117
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.018653 0.13658
## reps4 0.007253 0.08517 -1.00
## subid.1 RT_z 0.006540 0.08087
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.980992 0.99045
## Number of obs: 1132, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.012e-03 3.089e-02 1.741e+02 -0.098 0.9224
## RT_z 6.723e-02 3.514e-02 1.620e+01 1.913 0.0736 .
## reps1 1.184e-02 3.956e-02 1.690e+01 0.299 0.7683
## shockCond1 -2.963e-02 2.964e-02 1.124e+03 -1.000 0.3176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1
## RT_z -0.019
## reps1 -0.291 0.073
## shockCond1 -0.033 -0.023 0.008# double check simple effects
# contrasts(dCleanStandardized_HC_allfmri$group) = c(0,1)
# summary(lmer(scale(DAN_lh_sig_z) ~ group * (RT_z + reps) + shockCond +
# (-1 + reps|subid)+
# (-1 + RT_z|subid)+
# (1 |subid),
# data=dCleanStandardized_HC_allfmri))
# contrasts(dCleanStandardized_HC_allfmri$group) = c(1,0)
# summary(lmer(scale(DAN_lh_sig_z) ~ group * (RT_z + reps) + shockCond +
# (-1 + reps|subid)+
# (-1 + RT_z|subid)+
# (1 |subid),
# data=dCleanStandardized_HC_allfmri))
# contrasts(dCleanStandardized_HC_allfmri$group) = c(1,-1)
# interaction between shockcond and RT on DAN? in stress group
# remove subjs w/trial counts/bin < 6
dCleanStandardized_HC_allfmri %>% filter(group == 'stress') %>%
group_by(subid, shockCond) %>%
summarise(count=n()) %>%
ungroup() %>%
mutate(subid = factor(subid)) %>%
complete(subid, shockCond, fill = list(count= 0)) %>% filter(count < 6)## # A tibble: 1 x 3
## subid shockCond count
## <fctr> <fctr> <dbl>
## 1 ap173 threat 1summary(lmer(scale(DAN_lh_sig_z) ~ (RT_z + reps) + shockCond +
shockCond:RT_z +
(-1 + RT_z|subid)+
(-1 + shockCond|subid)+
(-1 + RT_z:shockCond|subid)+
(1 |subid),
control=lmerControl(optimizer="Nelder_Mead",
optCtrl=list(maxfun=2e5)),
data=dCleanStandardized_HC_allfmri %>% filter(group == 'stress',
subid != 'ap173')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## scale(DAN_lh_sig_z) ~ (RT_z + reps) + shockCond + shockCond:RT_z +
## (-1 + RT_z | subid) + (-1 + shockCond | subid) + (-1 + RT_z:shockCond |
## subid) + (1 | subid)
## Data:
## dCleanStandardized_HC_allfmri %>% filter(group == "stress", subid !=
## "ap173")
## Control:
## lmerControl(optimizer = "Nelder_Mead", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 3207.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2500 -0.5890 -0.0251 0.6313 3.2684
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid RT_z 2.250e-03 4.743e-02
## subid.1 shockCondsafe 0.000e+00 0.000e+00
## shockCondthreat 4.934e-12 2.221e-06 NaN
## subid.2 RT_z:shockCondsafe 1.799e-02 1.341e-01
## RT_z:shockCondthreat 6.093e-03 7.806e-02 -0.28
## subid.3 (Intercept) 0.000e+00 0.000e+00
## Residual 9.859e-01 9.929e-01
## Number of obs: 1124, groups: subid, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.112e-03 3.043e-02 1.100e+03 0.037 0.9708
## RT_z 6.420e-02 3.564e-02 1.540e+01 1.801 0.0913 .
## reps1 9.440e-03 3.067e-02 1.118e+03 0.308 0.7583
## shockCond1 -2.828e-02 2.972e-02 1.099e+03 -0.951 0.3416
## RT_z:shockCond1 1.581e-03 3.599e-02 1.700e+01 0.044 0.9655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1 shckC1
## RT_z -0.015
## reps1 -0.214 0.084
## shockCond1 -0.025 -0.019 0.001
## RT_z:shckC1 -0.024 0.113 0.026 0.003dCleanStandardized_HC_allfmri %>% filter(group == 'stress',
subid != 'ap173') %>%
group_by(subid, group) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 1 x 2
## group `n()`
## <fctr> <int>
## 1 stress 21# is the RT * rep interaction significant?
dCleanStandardized_HC_allfmri %>%
group_by(subid, reps) %>%
summarise(count=n()) %>%
ungroup() %>%
mutate(subid = factor(subid)) %>%
complete(subid, reps, fill = list(count= 0)) %>% filter(count < 6)## # A tibble: 3 x 3
## subid reps count
## <fctr> <fctr> <dbl>
## 1 ap110 2 3
## 2 ap158 2 2
## 3 ap173 2 2summary(lmer(scale(DAN_lh_sig_z) ~ group * (RT_z * reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(-1 + RT_z:reps|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri %>%
filter(!subid %in% c('ap110', 'ap158', 'ap173'))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(DAN_lh_sig_z) ~ group * (RT_z * reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (-1 + RT_z:reps | subid) +
## (1 | subid)
## Data: dCleanStandardized_HC_allfmri %>% filter(!subid %in% c("ap110",
## "ap158", "ap173"))
##
## REML criterion at convergence: 8192.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1885 -0.5911 -0.0067 0.6509 3.8416
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.002485 0.04985
## reps4 0.001160 0.03405 -1.00
## subid.1 RT_z 0.000000 0.00000
## subid.2 RT_z:reps2 0.002142 0.04628
## RT_z:reps4 0.002753 0.05247 1.00
## subid.3 (Intercept) 0.000000 0.00000
## Residual 0.979362 0.98963
## Number of obs: 2888, groups: subid, 41
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.890e-03 1.948e-02 1.467e+03 -0.251 0.8018
## group1 -3.272e-03 1.948e-02 1.467e+03 -0.168 0.8666
## RT_z 1.144e-01 2.089e-02 4.210e+01 5.475 2.23e-06 ***
## reps1 3.140e-02 2.066e-02 4.000e+01 1.520 0.1365
## shockCond1 -6.288e-03 1.845e-02 2.872e+03 -0.341 0.7333
## RT_z:reps1 6.934e-03 1.927e-02 2.593e+03 0.360 0.7190
## group1:RT_z 5.388e-02 2.089e-02 4.210e+01 2.579 0.0135 *
## group1:reps1 1.993e-02 2.066e-02 4.000e+01 0.965 0.3405
## group1:RT_z:reps1 -2.734e-03 1.927e-02 2.592e+03 -0.142 0.8872
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 RT_z:1 gr1:RT_ grp1:1
## group1 -0.223
## RT_z -0.035 0.007
## reps1 -0.211 0.061 0.105
## shockCond1 -0.014 0.008 -0.011 -0.012
## RT_z:reps1 0.123 -0.007 -0.111 -0.033 0.011
## group1:RT_z 0.006 -0.035 -0.189 -0.008 0.014 0.043
## group1:rps1 0.061 -0.211 -0.008 -0.198 -0.013 0.007 0.105
## grp1:RT_z:1 -0.006 0.123 0.043 0.007 -0.020 -0.223 -0.111 -0.033Plot combined lh networks
## collapse
dm_avg = dm_allfmri %>% filter(pcorr == 1) %>%
group_by(subid) %>%
mutate(rt_quintile = ntile(respRT, 5)) %>%
group_by(subid, group, rt_quintile) %>%
summarise(ccn = mean_(CCN_lh_sig),
dan = mean_(DAN_lh_sig),
count = n()) %>%
gather(key = 'network', value = 'signal', ccn:dan) %>%
mutate(network = replace(network, network=='ccn', 'CCN'),
network = replace(network, network=='dan', 'DAN'))
dfwc = summarySEwithin(dm_avg, measurevar="signal",
withinvars=c("rt_quintile", "network"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95)## Automatically converting the following non-factors to factors: rt_quintile, networkdfwc## group rt_quintile network N signal signal_norm sd
## 1 control 1 CCN 22 0.03230130 0.04668815 0.06275263
## 2 control 1 DAN 22 0.01262089 0.02700774 0.07062237
## 3 control 2 CCN 22 0.07582863 0.09021548 0.07688394
## 4 control 2 DAN 22 0.04543842 0.05982528 0.06851596
## 5 control 3 CCN 22 0.14757958 0.16196643 0.09290254
## 6 control 3 DAN 22 0.10225941 0.11664626 0.08514998
## 7 control 4 CCN 22 0.14597583 0.16036268 0.10035884
## 8 control 4 DAN 22 0.10304145 0.11742831 0.08069073
## 9 control 5 CCN 22 0.19266350 0.20705035 0.07902218
## 10 control 5 DAN 22 0.15007843 0.16446528 0.06707180
## 11 stress 1 CCN 22 0.10998110 0.09559425 0.17304944
## 12 stress 1 DAN 22 0.07594667 0.06155981 0.13707953
## 13 stress 2 CCN 22 0.12189648 0.10750963 0.13456509
## 14 stress 2 DAN 22 0.08136196 0.06697511 0.11640587
## 15 stress 3 CCN 22 0.14089084 0.12650399 0.10618655
## 16 stress 3 DAN 22 0.11177971 0.09739286 0.11820127
## 17 stress 4 CCN 22 0.21731931 0.20293246 0.13782212
## 18 stress 4 DAN 22 0.15811092 0.14372407 0.14472055
## 19 stress 5 CCN 22 0.15770078 0.14331393 0.10203830
## 20 stress 5 DAN 22 0.12053672 0.10614987 0.13673281
## se ci
## 1 0.01337891 0.02782296
## 2 0.01505674 0.03131220
## 3 0.01639171 0.03408843
## 4 0.01460765 0.03037828
## 5 0.01980689 0.04119068
## 6 0.01815404 0.03775338
## 7 0.02139658 0.04449662
## 8 0.01720332 0.03577626
## 9 0.01684759 0.03503647
## 10 0.01429976 0.02973797
## 11 0.03689427 0.07672582
## 12 0.02922545 0.06077766
## 13 0.02868937 0.05966282
## 14 0.02481781 0.05161147
## 15 0.02263905 0.04708048
## 16 0.02520060 0.05240751
## 17 0.02938377 0.06110690
## 18 0.03085452 0.06416550
## 19 0.02175464 0.04524125
## 20 0.02915153 0.06062393ann_text <- data.frame(rt_quintile = c(3.2, 1.8),
signal = c(0.08, .18),
group=c('control', 'stress'),
lab=c("control", "stress"),
network = factor(c('CCN', 'CCN'),
levels = c("CCN", "DAN")))
p2 = ggplot(dfwc, aes(x=rt_quintile, y=signal, group=group, color=group)) +
facet_grid(.~network)+
geom_line(position=pos_dodge, size=1) +
geom_errorbar(width=0, aes(ymin=signal-se,
ymax=signal+se), size=1, position=pos_dodge) +
geom_point(size=3, position=pos_dodge) +
ylab('Percent signal change')+
xlab('RT (quintiles)')+
ylim(-.015, .25)+
scale_color_manual(values=palette, guide = guide_legend(title = NULL)) +
theme_classic(base_size=14) +
geom_text(data = ann_text, aes(label=lab)) +
theme(legend.title=element_blank(),
legend.position="none",
strip.text = element_text(size=14),
strip.background = element_rect(colour="white", fill="white"))
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/respRTXfrontoparietalLH_quintile_HCrecollection_allfmri.jpg',
dpi=600, width = 4.7, height=2.5)Plot combined lh networks, full names
## collapse
dm_avg = dm_allfmri %>% filter(pcorr == 1) %>%
group_by(subid) %>%
mutate(rt_quintile = ntile(respRT, 5)) %>%
group_by(subid, group, rt_quintile) %>%
summarise(ccn = mean_(CCN_lh_sig),
dan = mean_(DAN_lh_sig),
count = n()) %>%
gather(key = 'network', value = 'signal', ccn:dan) %>%
mutate(network = replace(network, network=='ccn', 'Cognitive control'),
network = replace(network, network=='dan', 'Dorsal attention'))
dfwc = summarySEwithin(dm_avg, measurevar="signal",
withinvars=c("rt_quintile", "network"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95)## Automatically converting the following non-factors to factors: rt_quintile, networkdfwc## group rt_quintile network N signal signal_norm
## 1 control 1 Cognitive control 22 0.03230130 0.04668815
## 2 control 1 Dorsal attention 22 0.01262089 0.02700774
## 3 control 2 Cognitive control 22 0.07582863 0.09021548
## 4 control 2 Dorsal attention 22 0.04543842 0.05982528
## 5 control 3 Cognitive control 22 0.14757958 0.16196643
## 6 control 3 Dorsal attention 22 0.10225941 0.11664626
## 7 control 4 Cognitive control 22 0.14597583 0.16036268
## 8 control 4 Dorsal attention 22 0.10304145 0.11742831
## 9 control 5 Cognitive control 22 0.19266350 0.20705035
## 10 control 5 Dorsal attention 22 0.15007843 0.16446528
## 11 stress 1 Cognitive control 22 0.10998110 0.09559425
## 12 stress 1 Dorsal attention 22 0.07594667 0.06155981
## 13 stress 2 Cognitive control 22 0.12189648 0.10750963
## 14 stress 2 Dorsal attention 22 0.08136196 0.06697511
## 15 stress 3 Cognitive control 22 0.14089084 0.12650399
## 16 stress 3 Dorsal attention 22 0.11177971 0.09739286
## 17 stress 4 Cognitive control 22 0.21731931 0.20293246
## 18 stress 4 Dorsal attention 22 0.15811092 0.14372407
## 19 stress 5 Cognitive control 22 0.15770078 0.14331393
## 20 stress 5 Dorsal attention 22 0.12053672 0.10614987
## sd se ci
## 1 0.06275263 0.01337891 0.02782296
## 2 0.07062237 0.01505674 0.03131220
## 3 0.07688394 0.01639171 0.03408843
## 4 0.06851596 0.01460765 0.03037828
## 5 0.09290254 0.01980689 0.04119068
## 6 0.08514998 0.01815404 0.03775338
## 7 0.10035884 0.02139658 0.04449662
## 8 0.08069073 0.01720332 0.03577626
## 9 0.07902218 0.01684759 0.03503647
## 10 0.06707180 0.01429976 0.02973797
## 11 0.17304944 0.03689427 0.07672582
## 12 0.13707953 0.02922545 0.06077766
## 13 0.13456509 0.02868937 0.05966282
## 14 0.11640587 0.02481781 0.05161147
## 15 0.10618655 0.02263905 0.04708048
## 16 0.11820127 0.02520060 0.05240751
## 17 0.13782212 0.02938377 0.06110690
## 18 0.14472055 0.03085452 0.06416550
## 19 0.10203830 0.02175464 0.04524125
## 20 0.13673281 0.02915153 0.06062393p2 = ggplot(dfwc, aes(x=rt_quintile, y=signal, group=group, color=group)) +
facet_grid(.~network)+
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=signal-se,
ymax=signal+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('Percent signal change')+
xlab('RT (quintiles)')+
ylim(-.015, .25)+
scale_color_manual(values=palette, guide = guide_legend(title = NULL))
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/respRTXfrontoparietalLH_quintile_HCrecollection_allfmri_fullnames.jpg',
dpi=300, width = 8, height=4)
p2
Bilateral networks
CCN
summary(lmer(scale(CCN_sig_z) ~ group * (RT_z + reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(CCN_sig_z) ~ group * (RT_z + reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC_allfmri
##
## REML criterion at convergence: 8305.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9353 -0.6196 0.0292 0.6526 3.6600
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.000e+00 0.000e+00
## reps4 1.602e-16 1.266e-08 NaN
## subid.1 RT_z 2.995e-03 5.473e-02
## subid.2 (Intercept) 0.000e+00 0.000e+00
## Residual 9.850e-01 9.925e-01
## Number of obs: 2927, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.562e-03 1.923e-02 2.881e+03 -0.237 0.812512
## group1 -6.185e-03 1.923e-02 2.881e+03 -0.322 0.747754
## RT_z 8.793e-02 2.099e-02 3.270e+01 4.189 0.000199 ***
## reps1 2.588e-02 1.940e-02 2.915e+03 1.334 0.182342
## shockCond1 9.953e-03 1.838e-02 2.919e+03 0.542 0.588137
## group1:RT_z 5.584e-02 2.099e-02 3.270e+01 2.660 0.012010 *
## group1:reps1 3.495e-02 1.940e-02 2.915e+03 1.802 0.071701 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 g1:RT_
## group1 -0.234
## RT_z -0.021 0.004
## reps1 -0.200 0.064 0.106
## shockCond1 -0.016 0.014 -0.012 -0.015
## group1:RT_z 0.004 -0.021 -0.185 -0.010 0.011
## group1:rps1 0.064 -0.201 -0.009 -0.228 -0.014 0.106summary(lmer(scale(CCN_sig_z) ~ RT_z + reps + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri%>% filter(group == 'control')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## scale(CCN_sig_z) ~ RT_z + reps + shockCond + (-1 + reps | subid) +
## (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC_allfmri %>% filter(group == "control")
##
## REML criterion at convergence: 5075.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9481 -0.6062 0.0407 0.6464 3.6747
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.000e+00 0.000e+00
## reps4 6.711e-22 2.591e-11 NaN
## subid.1 RT_z 0.000e+00 0.000e+00
## subid.2 (Intercept) 0.000e+00 0.000e+00
## Residual 9.794e-01 9.897e-01
## Number of obs: 1795, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.049e-02 2.373e-02 1.791e+03 -0.442 0.6586
## RT_z 1.453e-01 2.357e-02 1.791e+03 6.165 8.67e-10 ***
## reps1 5.975e-02 2.398e-02 1.791e+03 2.491 0.0128 *
## shockCond1 7.280e-04 2.337e-02 1.791e+03 0.031 0.9752
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1
## RT_z -0.025
## reps1 -0.177 0.142
## shockCond1 -0.002 -0.002 -0.029summary(lmer(scale(CCN_sig_z) ~ RT_z + reps + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri %>% filter(group == 'stress'),
control=lmerControl(optimizer="Nelder_Mead",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## scale(CCN_sig_z) ~ RT_z + reps + shockCond + (-1 + reps | subid) +
## (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC_allfmri %>% filter(group == "stress")
## Control:
## lmerControl(optimizer = "Nelder_Mead", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 3227.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9361 -0.6480 0.0135 0.6603 3.1967
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.006532 0.08082
## reps4 0.002673 0.05170 -1.00
## subid.1 RT_z 0.011213 0.10589
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.986739 0.99335
## Number of obs: 1132, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.835e-04 3.052e-02 3.673e+02 0.029 0.977
## RT_z 3.132e-02 3.835e-02 1.670e+01 0.817 0.426
## reps1 -8.530e-03 3.419e-02 1.720e+01 -0.250 0.806
## shockCond1 2.641e-02 2.971e-02 1.126e+03 0.889 0.374
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1
## RT_z -0.018
## reps1 -0.245 0.076
## shockCond1 -0.032 -0.021 0.005# interaction between shockcond and RT on CCN? in stress group
# remove subjs w/trial counts/bin < 6
dCleanStandardized_HC_allfmri %>% filter(group == 'stress') %>%
group_by(subid, shockCond) %>%
summarise(count=n()) %>%
ungroup() %>%
mutate(subid = factor(subid)) %>%
complete(subid, shockCond, fill = list(count= 0)) %>% filter(count < 6)## # A tibble: 1 x 3
## subid shockCond count
## <fctr> <fctr> <dbl>
## 1 ap173 threat 1dCleanStandardized_HC_allfmri %>% filter(group == 'stress') %>%
group_by(subid, shockCond) %>%
summarise(count=n()) %>%
ungroup() %>%
mutate(subid = factor(subid)) %>%
complete(subid, shockCond, fill = list(count= 0))## # A tibble: 44 x 3
## subid shockCond count
## <fctr> <fctr> <dbl>
## 1 ap150 safe 33
## 2 ap150 threat 33
## 3 ap152 safe 18
## 4 ap152 threat 22
## 5 ap153 safe 17
## 6 ap153 threat 14
## 7 ap154 safe 20
## 8 ap154 threat 22
## 9 ap155 safe 27
## 10 ap155 threat 11
## # ... with 34 more rowssummary(lmer(scale(CCN_sig_z) ~ (RT_z + reps) + shockCond +
shockCond:RT_z +
(-1 + RT_z|subid)+ # remove reps random slope to converge
(-1 + shockCond|subid)+
(-1 + shockCond:RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri %>% filter(group == 'stress',
subid != 'ap173'),
control=lmerControl(optimizer="Nelder_Mead",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(CCN_sig_z) ~ (RT_z + reps) + shockCond + shockCond:RT_z +
## (-1 + RT_z | subid) + (-1 + shockCond | subid) + (-1 + shockCond:RT_z |
## subid) + (1 | subid)
## Data:
## dCleanStandardized_HC_allfmri %>% filter(group == "stress", subid !=
## "ap173")
## Control:
## lmerControl(optimizer = "Nelder_Mead", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 3207.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9475 -0.6394 0.0010 0.6419 3.2022
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid RT_z 2.789e-03 5.281e-02
## subid.1 shockCondsafe 0.000e+00 0.000e+00
## shockCondthreat 1.147e-10 1.071e-05 NaN
## subid.2 shockCondsafe:RT_z 2.672e-02 1.635e-01
## shockCondthreat:RT_z 9.376e-06 3.062e-03 1.00
## subid.3 (Intercept) 1.525e-10 1.235e-05
## Residual 9.860e-01 9.930e-01
## Number of obs: 1124, groups: subid, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.845e-03 3.040e-02 1.099e+03 0.094 0.925
## RT_z 3.392e-02 3.746e-02 1.530e+01 0.905 0.379
## reps1 -1.177e-02 3.066e-02 1.116e+03 -0.384 0.701
## shockCond1 2.658e-02 2.973e-02 1.104e+03 0.894 0.371
## RT_z:shockCond1 1.015e-02 3.510e-02 2.260e+01 0.289 0.775
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1 shckC1
## RT_z -0.015
## reps1 -0.214 0.079
## shockCond1 -0.022 -0.017 0.001
## RT_z:shckC1 -0.023 0.264 0.025 0.003dCleanStandardized_HC_allfmri %>% filter(group == 'stress',
subid != 'ap173') %>%
group_by(subid, group) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 1 x 2
## group `n()`
## <fctr> <int>
## 1 stress 21# is the RT * rep interaction significant?
dCleanStandardized_HC_allfmri %>%
group_by(subid, reps) %>%
summarise(count=n()) %>%
ungroup() %>%
mutate(subid = factor(subid)) %>%
complete(subid, reps, fill = list(count= 0)) %>% filter(count < 6)## # A tibble: 3 x 3
## subid reps count
## <fctr> <fctr> <dbl>
## 1 ap110 2 3
## 2 ap158 2 2
## 3 ap173 2 2summary(lmer(scale(CCN_sig_z) ~ group * (RT_z * reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(-1 + RT_z:reps|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri %>%
filter(!subid %in% c('ap110', 'ap158', 'ap173'))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(CCN_sig_z) ~ group * (RT_z * reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (-1 + RT_z:reps | subid) +
## (1 | subid)
## Data: dCleanStandardized_HC_allfmri %>% filter(!subid %in% c("ap110",
## "ap158", "ap173"))
##
## REML criterion at convergence: 8207.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9196 -0.6222 0.0233 0.6453 3.6728
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.000e+00 0.000e+00
## reps4 7.027e-16 2.651e-08 NaN
## subid.1 RT_z 0.000e+00 0.000e+00
## subid.2 RT_z:reps2 4.627e-03 6.802e-02
## RT_z:reps4 2.093e-03 4.575e-02 1.00
## subid.3 (Intercept) 0.000e+00 0.000e+00
## Residual 9.856e-01 9.928e-01
## Number of obs: 2888, groups: subid, 41
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.920e-03 1.948e-02 2.842e+03 -0.304 0.761216
## group1 -9.206e-03 1.948e-02 2.842e+03 -0.473 0.636531
## RT_z 8.709e-02 2.147e-02 2.950e+01 4.057 0.000334 ***
## reps1 2.344e-02 1.952e-02 2.874e+03 1.201 0.229785
## shockCond1 9.139e-03 1.851e-02 2.878e+03 0.494 0.621549
## RT_z:reps1 -1.145e-02 1.946e-02 4.965e+02 -0.589 0.556408
## group1:RT_z 5.566e-02 2.147e-02 2.950e+01 2.593 0.014664 *
## group1:reps1 3.475e-02 1.952e-02 2.874e+03 1.781 0.075095 .
## group1:RT_z:reps1 -2.329e-02 1.946e-02 4.960e+02 -1.197 0.231973
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 RT_z:1 gr1:RT_ grp1:1
## group1 -0.225
## RT_z -0.034 0.007
## reps1 -0.199 0.065 0.109
## shockCond1 -0.014 0.008 -0.011 -0.013
## RT_z:reps1 0.121 -0.007 -0.162 -0.034 0.011
## group1:RT_z 0.007 -0.034 -0.180 -0.008 0.014 0.042
## group1:rps1 0.065 -0.199 -0.008 -0.224 -0.013 0.007 0.108
## grp1:RT_z:1 -0.007 0.121 0.042 0.008 -0.020 -0.221 -0.162 -0.034dCleanStandardized_HC_allfmri %>% filter(!subid %in% c('ap110', 'ap158', 'ap173')) %>%
group_by(subid, group) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 20
## 2 stress 21DAN
summary(lmer(scale(DAN_sig_z) ~ group * (RT_z + reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(DAN_sig_z) ~ group * (RT_z + reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC_allfmri
##
## REML criterion at convergence: 8307.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9994 -0.6088 0.0075 0.6615 3.6197
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.005026 0.07089
## reps4 0.002307 0.04803 -1.00
## subid.1 RT_z 0.003214 0.05669
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.982744 0.99133
## Number of obs: 2927, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.918e-03 1.934e-02 1.002e+03 -0.409 0.6824
## group1 -2.928e-03 1.934e-02 1.001e+03 -0.151 0.8797
## RT_z 9.258e-02 2.114e-02 3.960e+01 4.378 8.5e-05 ***
## reps1 4.025e-02 2.170e-02 3.920e+01 1.855 0.0711 .
## shockCond1 -5.259e-03 1.837e-02 2.911e+03 -0.286 0.7747
## group1:RT_z 4.297e-02 2.114e-02 3.960e+01 2.032 0.0489 *
## group1:reps1 1.893e-02 2.170e-02 3.920e+01 0.872 0.3884
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 g1:RT_
## group1 -0.231
## RT_z -0.020 0.004
## reps1 -0.226 0.057 0.093
## shockCond1 -0.016 0.015 -0.012 -0.013
## group1:RT_z 0.004 -0.020 -0.183 -0.011 0.011
## group1:rps1 0.057 -0.226 -0.011 -0.183 -0.016 0.093# make sure it holds w/correlated int/slope -- reps RE changes...
fit2 = lmer(scale(DAN_sig_z) ~ group * (RT_z + reps) + shockCond +
(1+ reps + RT_z|subid),
data=dCleanStandardized_HC_allfmri)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 0.00690252 (tol =
## 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?coef(fit2)## $subid
## (Intercept) group1 RT_z reps1 shockCond1
## ap100 -0.0047793722 -0.002972828 0.06788118 0.023327370 -0.005342502
## ap101 -0.0071882063 -0.002972828 0.12140446 0.035917601 -0.005342502
## ap102 -0.0051616031 -0.002972828 0.09144731 0.025325173 -0.005342502
## ap103 -0.0081557719 -0.002972828 0.05560575 0.040974767 -0.005342502
## ap104 -0.0082389464 -0.002972828 0.04855497 0.041409495 -0.005342502
## ap105 -0.0081383152 -0.002972828 0.07862580 0.040883527 -0.005342502
## ap107 -0.0135281085 -0.002972828 0.13103518 0.069054309 -0.005342502
## ap108 -0.0094598920 -0.002972828 0.08912384 0.047790999 -0.005342502
## ap109 -0.0100887151 -0.002972828 0.09268312 0.051077663 -0.005342502
## ap110 -0.0129180653 -0.002972828 0.11615379 0.065865802 -0.005342502
## ap111 -0.0094956199 -0.002972828 0.08446214 0.047977738 -0.005342502
## ap113 -0.0060401448 -0.002972828 0.10943114 0.029917038 -0.005342502
## ap114 -0.0084654106 -0.002972828 0.06131887 0.042593153 -0.005342502
## ap115 -0.0099293504 -0.002972828 0.09976679 0.050244713 -0.005342502
## ap116 0.0061253441 -0.002972828 0.09844421 -0.033668209 -0.005342502
## ap117 -0.0063121393 -0.002972828 0.06464274 0.031338670 -0.005342502
## ap118 -0.0073155874 -0.002972828 0.10786519 0.036583383 -0.005342502
## ap119 -0.0088422534 -0.002972828 0.10302320 0.044562794 -0.005342502
## ap120 -0.0104587544 -0.002972828 0.08309731 0.053011745 -0.005342502
## ap121 -0.0046319626 -0.002972828 0.07610042 0.022556906 -0.005342502
## ap122 -0.0176893231 -0.002972828 0.15385972 0.090803691 -0.005342502
## ap150 0.0045216837 -0.002972828 0.06173774 -0.025286373 -0.005342502
## ap152 -0.0090462696 -0.002972828 0.11287340 0.045629123 -0.005342502
## ap153 -0.0043307945 -0.002972828 0.11195998 0.020982793 -0.005342502
## ap154 -0.0094305433 -0.002972828 0.04818004 0.047637603 -0.005342502
## ap155 -0.0028248014 -0.002972828 0.05212806 0.013111433 -0.005342502
## ap157 -0.0163377662 -0.002972828 0.08101901 0.083739521 -0.005342502
## ap158 -0.0065062295 -0.002972828 0.10530388 0.032353119 -0.005342502
## ap159 -0.0129263748 -0.002972828 0.10840311 0.065909233 -0.005342502
## ap160 -0.0048110112 -0.002972828 0.06830421 0.023492737 -0.005342502
## ap161 -0.0009353132 -0.002972828 0.08473583 0.003235662 -0.005342502
## ap162 -0.0081486747 -0.002972828 0.11225711 0.040937672 -0.005342502
## ap163 -0.0027542434 -0.002972828 0.07679163 0.012742648 -0.005342502
## ap164 -0.0107556412 -0.002972828 0.08409981 0.054563480 -0.005342502
## ap165 -0.0032368939 -0.002972828 0.08932587 0.015265313 -0.005342502
## ap166 -0.0157197818 -0.002972828 0.15064282 0.080509508 -0.005342502
## ap167 -0.0133477078 -0.002972828 0.09219483 0.068111410 -0.005342502
## ap168 -0.0055459546 -0.002972828 0.08164131 0.027334059 -0.005342502
## ap169 -0.0204576888 -0.002972828 0.12048611 0.105273083 -0.005342502
## ap170 -0.0042336529 -0.002972828 0.09983895 0.020475064 -0.005342502
## ap171 -0.0105410584 -0.002972828 0.11075443 0.053441922 -0.005342502
## ap172 -0.0098658217 -0.002972828 0.08087288 0.049912669 -0.005342502
## ap173 -0.0085016584 -0.002972828 0.10344073 0.042782609 -0.005342502
## ap174 -0.0079884593 -0.002972828 0.10814315 0.040100276 -0.005342502
## group1:RT_z group1:reps1
## ap100 0.04295962 0.01892237
## ap101 0.04295962 0.01892237
## ap102 0.04295962 0.01892237
## ap103 0.04295962 0.01892237
## ap104 0.04295962 0.01892237
## ap105 0.04295962 0.01892237
## ap107 0.04295962 0.01892237
## ap108 0.04295962 0.01892237
## ap109 0.04295962 0.01892237
## ap110 0.04295962 0.01892237
## ap111 0.04295962 0.01892237
## ap113 0.04295962 0.01892237
## ap114 0.04295962 0.01892237
## ap115 0.04295962 0.01892237
## ap116 0.04295962 0.01892237
## ap117 0.04295962 0.01892237
## ap118 0.04295962 0.01892237
## ap119 0.04295962 0.01892237
## ap120 0.04295962 0.01892237
## ap121 0.04295962 0.01892237
## ap122 0.04295962 0.01892237
## ap150 0.04295962 0.01892237
## ap152 0.04295962 0.01892237
## ap153 0.04295962 0.01892237
## ap154 0.04295962 0.01892237
## ap155 0.04295962 0.01892237
## ap157 0.04295962 0.01892237
## ap158 0.04295962 0.01892237
## ap159 0.04295962 0.01892237
## ap160 0.04295962 0.01892237
## ap161 0.04295962 0.01892237
## ap162 0.04295962 0.01892237
## ap163 0.04295962 0.01892237
## ap164 0.04295962 0.01892237
## ap165 0.04295962 0.01892237
## ap166 0.04295962 0.01892237
## ap167 0.04295962 0.01892237
## ap168 0.04295962 0.01892237
## ap169 0.04295962 0.01892237
## ap170 0.04295962 0.01892237
## ap171 0.04295962 0.01892237
## ap172 0.04295962 0.01892237
## ap173 0.04295962 0.01892237
## ap174 0.04295962 0.01892237
##
## attr(,"class")
## [1] "coef.mer"summary(fit2)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## scale(DAN_sig_z) ~ group * (RT_z + reps) + shockCond + (1 + reps +
## RT_z | subid)
## Data: dCleanStandardized_HC_allfmri
##
## REML criterion at convergence: 8307.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9981 -0.6100 0.0059 0.6624 3.6194
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.0001381 0.01175
## reps1 0.0037740 0.06143 -1.00
## RT_z 0.0034264 0.05854 -0.31 0.31
## Residual 0.9825694 0.99125
## Number of obs: 2927, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.055e-03 1.935e-02 9.742e+02 -0.416 0.6773
## group1 -2.973e-03 1.935e-02 9.733e+02 -0.154 0.8779
## RT_z 9.272e-02 2.127e-02 3.900e+01 4.359 9.24e-05 ***
## reps1 4.045e-02 2.184e-02 3.900e+01 1.852 0.0716 .
## shockCond1 -5.343e-03 1.837e-02 2.911e+03 -0.291 0.7712
## group1:RT_z 4.296e-02 2.127e-02 3.900e+01 2.020 0.0503 .
## group1:reps1 1.892e-02 2.184e-02 3.900e+01 0.866 0.3915
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 g1:RT_
## group1 -0.231
## RT_z -0.035 0.005
## reps1 -0.227 0.057 0.156
## shockCond1 -0.016 0.015 -0.011 -0.013
## group1:RT_z 0.005 -0.035 -0.182 -0.011 0.010
## group1:rps1 0.057 -0.228 -0.011 -0.182 -0.015 0.156
## convergence code: 0
## Model failed to converge with max|grad| = 0.00690252 (tol = 0.002, component 1)
## Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?summary(lmer(scale(DAN_sig_z) ~ RT_z + reps + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri%>% filter(group == 'control')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## scale(DAN_sig_z) ~ RT_z + reps + shockCond + (-1 + reps | subid) +
## (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC_allfmri %>% filter(group == "control")
##
## REML criterion at convergence: 5079.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9821 -0.6124 0.0222 0.6621 3.6206
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 3.420e-14 1.849e-07
## reps4 1.073e-14 1.036e-07 -1.00
## subid.1 RT_z 3.038e-03 5.512e-02
## subid.2 (Intercept) 0.000e+00 0.000e+00
## Residual 9.792e-01 9.895e-01
## Number of obs: 1795, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01012 0.02373 1776.80000 -0.427 0.6697
## RT_z 0.13640 0.02678 21.70000 5.094 4.39e-05 ***
## reps1 0.05725 0.02404 1785.20000 2.382 0.0173 *
## shockCond1 0.01059 0.02339 1789.10000 0.453 0.6507
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1
## RT_z -0.021
## reps1 -0.178 0.121
## shockCond1 -0.002 -0.001 -0.030summary(lmer(scale(DAN_sig_z) ~ RT_z + reps + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri %>% filter(group == 'stress'),
control=lmerControl(optimizer="Nelder_Mead",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## scale(DAN_sig_z) ~ RT_z + reps + shockCond + (-1 + reps | subid) +
## (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC_allfmri %>% filter(group == "stress")
## Control:
## lmerControl(optimizer = "Nelder_Mead", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 3223.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9479 -0.6095 -0.0157 0.6520 3.4683
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.024700 0.15716
## reps4 0.009742 0.09870 -1.00
## subid.1 RT_z 0.005569 0.07463
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.979902 0.98990
## Number of obs: 1132, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.413e-03 3.106e-02 1.478e+02 -0.174 0.862
## RT_z 5.250e-02 3.443e-02 1.420e+01 1.525 0.149
## reps1 2.187e-02 4.202e-02 1.800e+01 0.520 0.609
## shockCond1 -3.009e-02 2.963e-02 1.123e+03 -1.016 0.310
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1
## RT_z -0.020
## reps1 -0.308 0.070
## shockCond1 -0.033 -0.023 0.008# interaction between shockcond and RT on DAN? in stress group
# remove subjs w/trial counts/bin < 6
dCleanStandardized_HC_allfmri %>% filter(group == 'stress') %>%
group_by(subid, shockCond) %>%
summarise(count=n()) %>%
ungroup() %>%
mutate(subid = factor(subid)) %>%
complete(subid, shockCond, fill = list(count= 0)) %>% filter(count < 6)## # A tibble: 1 x 3
## subid shockCond count
## <fctr> <fctr> <dbl>
## 1 ap173 threat 1summary(lmer(scale(DAN_sig_z) ~ (RT_z + reps) + shockCond +
shockCond:RT_z +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(-1 + shockCond|subid)+
(-1 + shockCond:RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri %>% filter(group == 'stress',
subid != 'ap173'),
control=lmerControl(optimizer="Nelder_Mead",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(DAN_sig_z) ~ (RT_z + reps) + shockCond + shockCond:RT_z +
## (-1 + reps | subid) + (-1 + RT_z | subid) + (-1 + shockCond |
## subid) + (-1 + shockCond:RT_z | subid) + (1 | subid)
## Data:
## dCleanStandardized_HC_allfmri %>% filter(group == "stress", subid !=
## "ap173")
## Control:
## lmerControl(optimizer = "Nelder_Mead", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 3205.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9549 -0.6118 -0.0063 0.6491 3.4821
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 2.501e-02 1.581e-01
## reps4 9.989e-03 9.995e-02 -1.00
## subid.1 RT_z 3.924e-03 6.264e-02
## subid.2 shockCondsafe 5.656e-11 7.521e-06
## shockCondthreat 4.449e-11 6.670e-06 -1.00
## subid.3 shockCondsafe:RT_z 1.064e-02 1.032e-01
## shockCondthreat:RT_z 6.968e-04 2.640e-02 -1.00
## subid.4 (Intercept) 0.000e+00 0.000e+00
## Residual 9.770e-01 9.884e-01
## Number of obs: 1124, groups: subid, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.339e-03 3.116e-02 1.467e+02 -0.107 0.915
## RT_z 5.069e-02 3.450e-02 1.320e+01 1.469 0.165
## reps1 2.058e-02 4.244e-02 1.710e+01 0.485 0.634
## shockCond1 -2.927e-02 2.968e-02 1.097e+03 -0.986 0.324
## RT_z:shockCond1 -1.584e-02 3.352e-02 1.730e+01 -0.473 0.642
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1 shckC1
## RT_z -0.017
## reps1 -0.306 0.066
## shockCond1 -0.027 -0.020 0.010
## RT_z:shckC1 -0.022 0.111 0.023 0.005dCleanStandardized_HC_allfmri %>% filter(group == 'stress',
subid != 'ap173') %>%
group_by(subid, group) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 1 x 2
## group `n()`
## <fctr> <int>
## 1 stress 21# is the RT * rep interaction significant?
dCleanStandardized_HC_allfmri %>%
group_by(subid, reps) %>%
summarise(count=n()) %>%
ungroup() %>%
mutate(subid = factor(subid)) %>%
complete(subid, reps, fill = list(count= 0)) %>% filter(count < 6)## # A tibble: 3 x 3
## subid reps count
## <fctr> <fctr> <dbl>
## 1 ap110 2 3
## 2 ap158 2 2
## 3 ap173 2 2summary(lmer(scale(DAN_sig_z) ~ group * (RT_z * reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(-1 + RT_z:reps|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri %>%
filter(!subid %in% c('ap110', 'ap158', 'ap173'))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(DAN_sig_z) ~ group * (RT_z * reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (-1 + RT_z:reps | subid) +
## (1 | subid)
## Data: dCleanStandardized_HC_allfmri %>% filter(!subid %in% c("ap110",
## "ap158", "ap173"))
##
## REML criterion at convergence: 8213.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9938 -0.6076 0.0069 0.6602 3.6105
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.004831 0.06950
## reps4 0.002293 0.04788 -1.00
## subid.1 RT_z 0.000000 0.00000
## subid.2 RT_z:reps2 0.002352 0.04850
## RT_z:reps4 0.003711 0.06092 1.00
## subid.3 (Intercept) 0.000000 0.00000
## Residual 0.984821 0.99238
## Number of obs: 2888, groups: subid, 41
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.147e-03 1.959e-02 1.100e+03 -0.365 0.715301
## group1 -2.905e-03 1.959e-02 1.100e+03 -0.148 0.882124
## RT_z 8.662e-02 2.131e-02 4.110e+01 4.065 0.000212 ***
## reps1 3.905e-02 2.180e-02 3.880e+01 1.792 0.080979 .
## shockCond1 -5.661e-03 1.852e-02 2.870e+03 -0.306 0.759812
## RT_z:reps1 2.785e-03 1.935e-02 1.789e+03 0.144 0.885566
## group1:RT_z 4.129e-02 2.131e-02 4.110e+01 1.938 0.059552 .
## group1:reps1 1.796e-02 2.180e-02 3.880e+01 0.824 0.415029
## group1:RT_z:reps1 -3.808e-03 1.935e-02 1.790e+03 -0.197 0.844023
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 RT_z:1 gr1:RT_ grp1:1
## group1 -0.223
## RT_z -0.034 0.007
## reps1 -0.220 0.058 0.099
## shockCond1 -0.015 0.008 -0.011 -0.011
## RT_z:reps1 0.122 -0.007 -0.097 -0.031 0.011
## group1:RT_z 0.007 -0.034 -0.182 -0.009 0.014 0.043
## group1:rps1 0.058 -0.221 -0.008 -0.178 -0.014 0.007 0.098
## grp1:RT_z:1 -0.006 0.122 0.043 0.007 -0.020 -0.222 -0.097 -0.031fit = lmer(scale(DAN_sig_z) ~ group * (RT_z * reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(-1 + RT_z:reps|subid)+
(1 |subid),
data=dCleanStandardized_HC_allfmri %>%
filter(!subid %in% c('ap110', 'ap158', 'ap173')))
coef(fit)## $subid
## reps2 reps4 RT_z:reps2 RT_z:reps4 (Intercept)
## ap100 0.012293203 -0.0084694959 -0.0141922757 -0.0178259953 -0.007147128
## ap101 0.013669566 -0.0094177518 0.0273263231 0.0343228189 -0.007147128
## ap102 0.017114075 -0.0117908727 0.0020775534 0.0026094799 -0.007147128
## ap103 -0.011752974 0.0080973012 -0.0308194870 -0.0387103551 -0.007147128
## ap104 -0.013353940 0.0092002990 -0.0342700021 -0.0430443229 -0.007147128
## ap105 -0.004912647 0.0033846058 -0.0090027216 -0.0113077336 -0.007147128
## ap107 -0.023615667 0.0162701941 0.0288655113 0.0362560931 -0.007147128
## ap108 -0.010238193 0.0070536815 -0.0055017252 -0.0069103596 -0.007147128
## ap109 -0.012994024 0.0089523319 0.0027962545 0.0035121936 -0.007147128
## ap111 -0.011917392 0.0082105781 -0.0059001742 -0.0074108254 -0.007147128
## ap113 0.017604544 -0.0121287853 0.0149049887 0.0187211878 -0.007147128
## ap114 -0.012236719 0.0084305811 -0.0280142585 -0.0351868898 -0.007147128
## ap115 -0.009954533 0.0068582512 0.0076838707 0.0096512108 -0.007147128
## ap116 0.087819839 -0.0605041483 0.0199322777 0.0250356388 -0.007147128
## ap117 0.001717520 -0.0011832987 -0.0224964494 -0.0282563283 -0.007147128
## ap118 0.009063445 -0.0062443293 0.0168404922 0.0211522480 -0.007147128
## ap119 -0.001962590 0.0013521415 0.0065642167 0.0082448860 -0.007147128
## ap120 -0.017973065 0.0123826806 -0.0109327155 -0.0137318736 -0.007147128
## ap121 0.015331191 -0.0105625410 -0.0101038260 -0.0126907593 -0.007147128
## ap122 -0.043701638 0.0301085768 0.0442421468 0.0555696859 -0.007147128
## ap150 0.068587094 -0.0472536018 -0.0138917608 -0.0174485381 -0.007147128
## ap152 0.001112299 -0.0007663267 0.0169125743 0.0212427857 -0.007147128
## ap153 0.030104825 -0.0207409489 0.0211628178 0.0265812403 -0.007147128
## ap154 -0.023721066 0.0163428091 -0.0400612848 -0.0503183768 -0.007147128
## ap155 0.018397186 -0.0126748817 -0.0274613371 -0.0344924012 -0.007147128
## ap157 -0.055369807 0.0381474510 -0.0192384141 -0.0241641219 -0.007147128
## ap159 -0.024524567 0.0168963876 0.0061759971 0.0077572686 -0.007147128
## ap160 0.012021921 -0.0082825943 -0.0193255061 -0.0242735126 -0.007147128
## ap161 0.041503869 -0.0285944075 0.0011097597 0.0013938971 -0.007147128
## ap162 0.005994880 -0.0041302183 0.0199318958 0.0250351592 -0.007147128
## ap163 0.028278696 -0.0194828235 -0.0088529183 -0.0111195755 -0.007147128
## ap164 -0.019409900 0.0133725988 -0.0082015673 -0.0103014557 -0.007147128
## ap165 0.028878169 -0.0198958350 0.0004773888 0.0005996171 -0.007147128
## ap166 -0.029401809 0.0202566007 0.0440300579 0.0553032948 -0.007147128
## ap167 -0.033492042 0.0230745977 -0.0045153363 -0.0056714205 -0.007147128
## ap168 0.011764264 -0.0081050795 -0.0062635896 -0.0078672880 -0.007147128
## ap169 -0.067559771 0.0465458197 0.0112136112 0.0140846883 -0.007147128
## ap170 0.025956125 -0.0178826702 0.0107612467 0.0135165028 -0.007147128
## ap171 -0.008775318 0.0060458226 0.0154469664 0.0194019307 -0.007147128
## ap172 -0.015381118 0.0105969389 -0.0132021369 -0.0165823463 -0.007147128
## ap174 0.005036070 -0.0034696387 0.0137915358 0.0173226520 -0.007147128
## group1 RT_z reps1 shockCond1 RT_z:reps1
## ap100 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap101 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap102 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap103 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap104 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap105 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap107 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap108 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap109 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap111 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap113 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap114 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap115 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap116 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap117 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap118 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap119 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap120 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap121 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap122 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap150 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap152 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap153 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap154 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap155 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap157 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap159 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap160 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap161 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap162 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap163 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap164 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap165 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap166 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap167 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap168 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap169 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap170 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap171 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap172 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## ap174 -0.002905196 0.08661835 0.03905343 -0.005661439 0.002784899
## group1:RT_z group1:reps1 group1:RT_z:reps1
## ap100 0.0412895 0.01795923 -0.003807805
## ap101 0.0412895 0.01795923 -0.003807805
## ap102 0.0412895 0.01795923 -0.003807805
## ap103 0.0412895 0.01795923 -0.003807805
## ap104 0.0412895 0.01795923 -0.003807805
## ap105 0.0412895 0.01795923 -0.003807805
## ap107 0.0412895 0.01795923 -0.003807805
## ap108 0.0412895 0.01795923 -0.003807805
## ap109 0.0412895 0.01795923 -0.003807805
## ap111 0.0412895 0.01795923 -0.003807805
## ap113 0.0412895 0.01795923 -0.003807805
## ap114 0.0412895 0.01795923 -0.003807805
## ap115 0.0412895 0.01795923 -0.003807805
## ap116 0.0412895 0.01795923 -0.003807805
## ap117 0.0412895 0.01795923 -0.003807805
## ap118 0.0412895 0.01795923 -0.003807805
## ap119 0.0412895 0.01795923 -0.003807805
## ap120 0.0412895 0.01795923 -0.003807805
## ap121 0.0412895 0.01795923 -0.003807805
## ap122 0.0412895 0.01795923 -0.003807805
## ap150 0.0412895 0.01795923 -0.003807805
## ap152 0.0412895 0.01795923 -0.003807805
## ap153 0.0412895 0.01795923 -0.003807805
## ap154 0.0412895 0.01795923 -0.003807805
## ap155 0.0412895 0.01795923 -0.003807805
## ap157 0.0412895 0.01795923 -0.003807805
## ap159 0.0412895 0.01795923 -0.003807805
## ap160 0.0412895 0.01795923 -0.003807805
## ap161 0.0412895 0.01795923 -0.003807805
## ap162 0.0412895 0.01795923 -0.003807805
## ap163 0.0412895 0.01795923 -0.003807805
## ap164 0.0412895 0.01795923 -0.003807805
## ap165 0.0412895 0.01795923 -0.003807805
## ap166 0.0412895 0.01795923 -0.003807805
## ap167 0.0412895 0.01795923 -0.003807805
## ap168 0.0412895 0.01795923 -0.003807805
## ap169 0.0412895 0.01795923 -0.003807805
## ap170 0.0412895 0.01795923 -0.003807805
## ap171 0.0412895 0.01795923 -0.003807805
## ap172 0.0412895 0.01795923 -0.003807805
## ap174 0.0412895 0.01795923 -0.003807805
##
## attr(,"class")
## [1] "coef.mer"summary(fit)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(DAN_sig_z) ~ group * (RT_z * reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (-1 + RT_z:reps | subid) +
## (1 | subid)
## Data: dCleanStandardized_HC_allfmri %>% filter(!subid %in% c("ap110",
## "ap158", "ap173"))
##
## REML criterion at convergence: 8213.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9938 -0.6076 0.0069 0.6602 3.6105
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.004831 0.06950
## reps4 0.002293 0.04788 -1.00
## subid.1 RT_z 0.000000 0.00000
## subid.2 RT_z:reps2 0.002352 0.04850
## RT_z:reps4 0.003711 0.06092 1.00
## subid.3 (Intercept) 0.000000 0.00000
## Residual 0.984821 0.99238
## Number of obs: 2888, groups: subid, 41
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.147e-03 1.959e-02 1.100e+03 -0.365 0.715301
## group1 -2.905e-03 1.959e-02 1.100e+03 -0.148 0.882124
## RT_z 8.662e-02 2.131e-02 4.110e+01 4.065 0.000212 ***
## reps1 3.905e-02 2.180e-02 3.880e+01 1.792 0.080979 .
## shockCond1 -5.661e-03 1.852e-02 2.870e+03 -0.306 0.759812
## RT_z:reps1 2.785e-03 1.935e-02 1.789e+03 0.144 0.885566
## group1:RT_z 4.129e-02 2.131e-02 4.110e+01 1.938 0.059552 .
## group1:reps1 1.796e-02 2.180e-02 3.880e+01 0.824 0.415029
## group1:RT_z:reps1 -3.808e-03 1.935e-02 1.790e+03 -0.197 0.844023
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 RT_z:1 gr1:RT_ grp1:1
## group1 -0.223
## RT_z -0.034 0.007
## reps1 -0.220 0.058 0.099
## shockCond1 -0.015 0.008 -0.011 -0.011
## RT_z:reps1 0.122 -0.007 -0.097 -0.031 0.011
## group1:RT_z 0.007 -0.034 -0.182 -0.009 0.014 0.043
## group1:rps1 0.058 -0.221 -0.008 -0.178 -0.014 0.007 0.098
## grp1:RT_z:1 -0.006 0.122 0.043 0.007 -0.020 -0.222 -0.097 -0.031dCleanStandardized_HC_allfmri %>% filter(!subid %in% c('ap110', 'ap158', 'ap173')) %>%
group_by(subid, group) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 20
## 2 stress 212b (ii): Excluding 2 subjs w/poor vtc localizer (trial-wise criteria from above)
Is VTC reinstatement modulated?
summary(lmer(ERActUnsigned ~ group * (RT_z * reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: ERActUnsigned ~ group * (RT_z * reps) + shockCond + (-1 + reps |
## subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC
##
## REML criterion at convergence: 7940
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4449 -0.6519 -0.0346 0.6553 3.4585
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.005098 0.07140
## reps4 0.002325 0.04822 -1.00
## subid.1 RT_z 0.020564 0.14340
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.970349 0.98506
## Number of obs: 2797, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.062e-03 2.019e-02 1.005e+03 -0.251 0.8021
## group1 2.853e-03 2.019e-02 1.004e+03 0.141 0.8876
## RT_z 5.266e-02 3.068e-02 3.640e+01 1.716 0.0946 .
## reps1 3.912e-02 2.253e-02 4.630e+01 1.736 0.0892 .
## shockCond1 6.336e-03 1.874e-02 2.768e+03 0.338 0.7353
## RT_z:reps1 3.021e-02 2.009e-02 2.739e+03 1.503 0.1329
## group1:RT_z 6.120e-02 3.068e-02 3.640e+01 1.995 0.0536 .
## group1:reps1 -1.024e-02 2.254e-02 4.630e+01 -0.454 0.6516
## group1:RT_z:reps1 -1.092e-02 2.010e-02 2.739e+03 -0.543 0.5869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 RT_z:1 gr1:RT_ grp1:1
## group1 -0.287
## RT_z -0.030 0.011
## reps1 -0.233 0.074 0.079
## shockCond1 -0.024 0.022 -0.012 -0.007
## RT_z:reps1 0.135 -0.032 -0.099 -0.031 0.011
## group1:RT_z 0.011 -0.030 -0.152 -0.026 0.012 0.035
## group1:rps1 0.074 -0.233 -0.026 -0.242 -0.023 0.014 0.079
## grp1:RT_z:1 -0.031 0.135 0.035 0.014 -0.020 -0.285 -0.099 -0.031Modulation of HC recollection decisions by RT
LH CCN
mean(dCleanStandardized_HC$RT_z)## [1] -1.873681e-17sd(dCleanStandardized_HC$RT_z)## [1] 1mean(dCleanStandardized_HC %>% filter(subid =='ap150') %>% pull(RT_z))## [1] 1.263835e-16sd(dCleanStandardized_HC %>% filter(subid =='ap150') %>% pull(RT_z))## [1] 1.007414dCleanStandardized_HC %>% group_by(subid, group) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 22
## 2 stress 20# first lh only
fit = lmer(scale(CCN_lh_sig_z) ~ group * (RT_z + reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC)
summary(fit)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(CCN_lh_sig_z) ~ group * (RT_z + reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC
##
## REML criterion at convergence: 7910.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8505 -0.6201 0.0141 0.6403 3.7477
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.000e+00 0.000e+00
## reps4 1.229e-16 1.109e-08 NaN
## subid.1 RT_z 3.309e-03 5.753e-02
## subid.2 (Intercept) 0.000e+00 0.000e+00
## Residual 9.751e-01 9.875e-01
## Number of obs: 2797, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.564e-03 1.990e-02 2.755e+03 -0.229 0.8186
## group1 -6.656e-03 1.990e-02 2.756e+03 -0.334 0.7381
## RT_z 1.230e-01 2.191e-02 3.560e+01 5.613 2.38e-06 ***
## reps1 2.531e-02 2.010e-02 2.788e+03 1.259 0.2081
## shockCond1 1.368e-02 1.871e-02 2.789e+03 0.731 0.4650
## group1:RT_z 5.828e-02 2.191e-02 3.560e+01 2.661 0.0116 *
## group1:reps1 3.806e-02 2.010e-02 2.788e+03 1.893 0.0584 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 g1:RT_
## group1 -0.292
## RT_z -0.023 0.008
## reps1 -0.204 0.078 0.117
## shockCond1 -0.026 0.024 -0.014 -0.008
## group1:RT_z 0.008 -0.023 -0.241 -0.029 0.013
## group1:rps1 0.079 -0.205 -0.029 -0.288 -0.021 0.116# make sure holds when removing rep effect
# summary(lmer(scale(CCN_lh_sig_z) ~ group * (RT_z + reps) + shockCond +
# (-1 + RT_z|subid)+
# (1 |subid),
# data=dCleanStandardized_HC))
# explore interaction of group * rt
summary(lmer(scale(CCN_lh_sig_z) ~ RT_z + reps + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC%>% filter(group == 'control')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(CCN_lh_sig_z) ~ RT_z + reps + shockCond + (-1 + reps |
## subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC %>% filter(group == "control")
##
## REML criterion at convergence: 5052.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8479 -0.6044 0.0358 0.6425 3.7712
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.000e+00 0.000e+00
## reps4 3.092e-17 5.561e-09 NaN
## subid.1 RT_z 2.649e-03 5.146e-02
## subid.2 (Intercept) 0.000e+00 0.000e+00
## Residual 9.649e-01 9.823e-01
## Number of obs: 1795, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.116e-02 2.356e-02 1.772e+03 -0.474 0.63579
## RT_z 1.813e-01 2.624e-02 1.630e+01 6.909 3.16e-06 ***
## reps1 6.339e-02 2.386e-02 1.782e+03 2.657 0.00795 **
## shockCond1 4.924e-03 2.322e-02 1.789e+03 0.212 0.83207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1
## RT_z -0.022
## reps1 -0.177 0.123
## shockCond1 -0.002 -0.001 -0.030summary(lmer(scale(CCN_lh_sig_z) ~ RT_z + reps + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC%>% filter(group == 'stress'),
control=lmerControl(optimizer="Nelder_Mead",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(CCN_lh_sig_z) ~ RT_z + reps + shockCond + (-1 + reps |
## subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC %>% filter(group == "stress")
## Control:
## lmerControl(optimizer = "Nelder_Mead", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 2856.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7611 -0.6446 -0.0120 0.6406 3.1833
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.008127 0.09015
## reps4 0.003236 0.05689 -1.00
## subid.1 RT_z 0.004847 0.06962
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.988153 0.99406
## Number of obs: 1002, groups: subid, 20
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.933e-04 3.259e-02 2.811e+02 0.030 0.9757
## RT_z 6.398e-02 3.604e-02 1.530e+01 1.775 0.0957 .
## reps1 -1.233e-02 3.712e-02 1.560e+01 -0.332 0.7442
## shockCond1 2.962e-02 3.160e-02 9.962e+02 0.937 0.3488
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1
## RT_z -0.023
## reps1 -0.256 0.100
## shockCond1 -0.053 -0.029 0.018# check to make sure simple effects are same
# contrasts(dCleanStandardized_HC$group) = c(0,1)
# summary(lmer(scale(CCN_lh_sig_z) ~ group * (RT_z + reps) + shockCond +
# (-1 + reps|subid)+
# (-1 + RT_z|subid)+
# (1 |subid),
# data=dCleanStandardized_HC))
#
# contrasts(dCleanStandardized_HC$group) = c(1,0)
# summary(lmer(scale(CCN_lh_sig_z) ~ group * (RT_z + reps) + shockCond +
# (-1 + reps|subid)+
# (-1 + RT_z|subid)+
# (1 |subid),
# data=dCleanStandardized_HC))
# contrasts(dCleanStandardized_HC$group) = c(1,-1)
# interaction between shockcond and RT on CCN? in stress group
# remove subjs w/trial counts/bin < 6
dCleanStandardized_HC %>% filter(group == 'stress') %>%
group_by(subid, shockCond) %>%
summarise(count=n()) %>%
ungroup() %>%
mutate(subid = factor(subid)) %>%
complete(subid, shockCond, fill = list(count= 0)) %>% filter(count < 6)## # A tibble: 1 x 3
## subid shockCond count
## <fctr> <fctr> <dbl>
## 1 ap173 threat 1summary(lmer(scale(CCN_lh_sig_z) ~ (RT_z + reps) + shockCond +
shockCond:RT_z +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(-1 + shockCond|subid)+
(-1 + shockCond:RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC %>% filter(group == 'stress',
subid != 'ap173')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## scale(CCN_lh_sig_z) ~ (RT_z + reps) + shockCond + shockCond:RT_z +
## (-1 + reps | subid) + (-1 + RT_z | subid) + (-1 + shockCond |
## subid) + (-1 + shockCond:RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC %>% filter(group == "stress", subid !=
## "ap173")
##
## REML criterion at convergence: 2832
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8162 -0.6450 -0.0056 0.6411 3.2381
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 4.022e-03 6.342e-02
## reps4 1.634e-03 4.043e-02 -1.00
## subid.1 RT_z 0.000e+00 0.000e+00
## subid.2 shockCondsafe 0.000e+00 0.000e+00
## shockCondthreat 7.915e-15 8.896e-08 NaN
## subid.3 shockCondsafe:RT_z 3.323e-02 1.823e-01
## shockCondthreat:RT_z 1.429e-03 3.780e-02 -1.00
## subid.4 (Intercept) 0.000e+00 0.000e+00
## Residual 9.772e-01 9.885e-01
## Number of obs: 994, groups: subid, 19
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.002146 0.032479 408.100000 0.066 0.9474
## RT_z 0.069173 0.036358 32.300000 1.903 0.0661 .
## reps1 -0.015523 0.035029 14.200000 -0.443 0.6643
## shockCond1 0.031493 0.031482 973.900000 1.000 0.3174
## RT_z:shockCond1 0.029239 0.041072 20.600000 0.712 0.4845
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1 shckC1
## RT_z -0.018
## reps1 -0.236 0.098
## shockCond1 -0.042 -0.023 0.018
## RT_z:shckC1 -0.026 0.289 0.024 0.004dCleanStandardized_HC %>% filter(group == 'stress',
subid != 'ap173') %>%
group_by(subid, group) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 1 x 2
## group `n()`
## <fctr> <int>
## 1 stress 19# is the RT * rep interaction significant?
dCleanStandardized_HC %>%
group_by(subid, reps) %>%
summarise(count=n()) %>%
ungroup() %>%
mutate(subid = factor(subid)) %>%
complete(subid, reps, fill = list(count= 0)) %>% filter(count < 6)## # A tibble: 3 x 3
## subid reps count
## <fctr> <fctr> <dbl>
## 1 ap110 2 3
## 2 ap158 2 2
## 3 ap173 2 2summary(lmer(scale(CCN_lh_sig_z) ~ group * (RT_z * reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(-1 + RT_z:reps|subid)+
(1 |subid),
data=dCleanStandardized_HC %>%
filter(!subid %in% c('ap110', 'ap158', 'ap173'))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(CCN_lh_sig_z) ~ group * (RT_z * reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (-1 + RT_z:reps | subid) +
## (1 | subid)
## Data: dCleanStandardized_HC %>% filter(!subid %in% c("ap110", "ap158",
## "ap173"))
##
## REML criterion at convergence: 7815.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8424 -0.6222 0.0134 0.6445 3.7462
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 8.169e-15 9.038e-08
## reps4 1.166e-14 1.080e-07 -1.00
## subid.1 RT_z 3.916e-10 1.979e-05
## subid.2 RT_z:reps2 3.552e-03 5.960e-02
## RT_z:reps4 3.068e-03 5.539e-02 1.00
## subid.3 (Intercept) 0.000e+00 0.000e+00
## Residual 9.769e-01 9.884e-01
## Number of obs: 2758, groups: subid, 39
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.246e-03 2.020e-02 2.716e+03 -0.210 0.8336
## group1 -8.325e-03 2.020e-02 2.716e+03 -0.412 0.6803
## RT_z 1.214e-01 2.221e-02 3.370e+01 5.468 4.37e-06 ***
## reps1 2.332e-02 2.023e-02 2.746e+03 1.153 0.2491
## shockCond1 1.360e-02 1.886e-02 2.748e+03 0.721 0.4709
## RT_z:reps1 -8.333e-04 2.003e-02 1.764e+03 -0.042 0.9668
## group1:RT_z 5.564e-02 2.221e-02 3.370e+01 2.505 0.0173 *
## group1:reps1 3.727e-02 2.023e-02 2.746e+03 1.842 0.0656 .
## group1:RT_z:reps1 -1.490e-02 2.004e-02 1.762e+03 -0.744 0.4571
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 RT_z:1 gr1:RT_ grp1:1
## group1 -0.286
## RT_z -0.038 0.012
## reps1 -0.203 0.080 0.121
## shockCond1 -0.024 0.017 -0.012 -0.006
## RT_z:reps1 0.135 -0.028 -0.120 -0.038 0.009
## group1:RT_z 0.012 -0.038 -0.237 -0.028 0.015 0.041
## group1:rps1 0.081 -0.203 -0.027 -0.284 -0.019 0.013 0.121
## grp1:RT_z:1 -0.028 0.135 0.041 0.014 -0.018 -0.281 -0.120 -0.038dCleanStandardized_HC %>% filter(!subid %in% c('ap110', 'ap158', 'ap173')) %>%
group_by(subid, group) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 20
## 2 stress 19Slopes for each subj
datalist = list()
i = 1
for (subject in unique(dCleanStandardized_HC$subid)){
print(subject)
fit = lm(CCN_lh_sig_z ~ RT_z + reps + shockCond,
data=dCleanStandardized_HC %>% filter(subid == subject))
print(fit$coefficients['RT_z'])
dat = data.frame(subid=subject,
coef = fit$coefficients['RT_z'])
datalist[[i]] <- dat
i = 1+i
}## [1] "ap100"
## RT_z
## 0.1845116
## [1] "ap101"
## RT_z
## 0.2047765
## [1] "ap102"
## RT_z
## 0.1459256
## [1] "ap103"
## RT_z
## 0.1639334
## [1] "ap104"
## RT_z
## 0.1751052
## [1] "ap105"
## RT_z
## 0.3370491
## [1] "ap107"
## RT_z
## 0.3571874
## [1] "ap108"
## RT_z
## 0.1790157
## [1] "ap109"
## RT_z
## 0.06314569
## [1] "ap110"
## RT_z
## 0.3037418
## [1] "ap111"
## RT_z
## 0.1025788
## [1] "ap113"
## RT_z
## 0.2607868
## [1] "ap114"
## RT_z
## 0.1346667
## [1] "ap115"
## RT_z
## -0.2408414
## [1] "ap116"
## RT_z
## 0.1734513
## [1] "ap117"
## RT_z
## 0.185716
## [1] "ap118"
## RT_z
## 0.1506152
## [1] "ap119"
## RT_z
## 0.2439138
## [1] "ap120"
## RT_z
## 0.1436209
## [1] "ap121"
## RT_z
## 0.02858525
## [1] "ap122"
## RT_z
## 0.1926783
## [1] "ap158"
## RT_z
## 0.6110446
## [1] "ap150"
## RT_z
## -0.004798299
## [1] "ap152"
## RT_z
## 0.2222908
## [1] "ap153"
## RT_z
## 0.02737265
## [1] "ap154"
## RT_z
## -0.2121761
## [1] "ap155"
## RT_z
## -0.1092804
## [1] "ap157"
## RT_z
## 0.02666674
## [1] "ap159"
## RT_z
## 0.1598098
## [1] "ap160"
## RT_z
## -0.2262615
## [1] "ap161"
## RT_z
## 0.008245208
## [1] "ap162"
## RT_z
## 0.1630138
## [1] "ap163"
## RT_z
## 0.01060648
## [1] "ap164"
## RT_z
## 0.205875
## [1] "ap165"
## RT_z
## 0.07193465
## [1] "ap166"
## RT_z
## 0.3259992
## [1] "ap167"
## RT_z
## -0.03177933
## [1] "ap169"
## RT_z
## 0.04059666
## [1] "ap170"
## RT_z
## 0.09091969
## [1] "ap171"
## RT_z
## 0.3231811
## [1] "ap172"
## RT_z
## 0.1333716
## [1] "ap173"
## RT_z
## -0.1138961slopes_ccn_rt <- dplyr::bind_rows(datalist)## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characterslopes_ccn_rt## subid coef
## 1 ap100 0.184511557
## 2 ap101 0.204776511
## 3 ap102 0.145925568
## 4 ap103 0.163933423
## 5 ap104 0.175105217
## 6 ap105 0.337049069
## 7 ap107 0.357187417
## 8 ap108 0.179015663
## 9 ap109 0.063145689
## 10 ap110 0.303741792
## 11 ap111 0.102578839
## 12 ap113 0.260786820
## 13 ap114 0.134666679
## 14 ap115 -0.240841380
## 15 ap116 0.173451254
## 16 ap117 0.185715962
## 17 ap118 0.150615230
## 18 ap119 0.243913788
## 19 ap120 0.143620858
## 20 ap121 0.028585255
## 21 ap122 0.192678267
## 22 ap158 0.611044591
## 23 ap150 -0.004798299
## 24 ap152 0.222290779
## 25 ap153 0.027372646
## 26 ap154 -0.212176088
## 27 ap155 -0.109280428
## 28 ap157 0.026666741
## 29 ap159 0.159809764
## 30 ap160 -0.226261477
## 31 ap161 0.008245208
## 32 ap162 0.163013822
## 33 ap163 0.010606480
## 34 ap164 0.205875041
## 35 ap165 0.071934655
## 36 ap166 0.325999195
## 37 ap167 -0.031779329
## 38 ap169 0.040596662
## 39 ap170 0.090919687
## 40 ap171 0.323181115
## 41 ap172 0.133371621
## 42 ap173 -0.113896059LH DAN
### DAN
summary(lmer(scale(DAN_lh_sig_z) ~ group * (RT_z + reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(DAN_lh_sig_z) ~ group * (RT_z + reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC
##
## REML criterion at convergence: 7916.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1961 -0.6013 -0.0067 0.6499 3.8548
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.003463 0.05884
## reps4 0.001566 0.03957 -1.00
## subid.1 RT_z 0.002964 0.05444
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.975570 0.98771
## Number of obs: 2797, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.010e-03 2.001e-02 1.158e+03 -0.350 0.7262
## group1 -2.715e-03 2.001e-02 1.156e+03 -0.136 0.8921
## RT_z 1.190e-01 2.172e-02 4.010e+01 5.481 2.51e-06 ***
## reps1 3.612e-02 2.176e-02 3.890e+01 1.660 0.1049
## shockCond1 -5.597e-03 1.873e-02 2.782e+03 -0.299 0.7651
## group1:RT_z 5.700e-02 2.172e-02 4.010e+01 2.625 0.0122 *
## group1:reps1 1.726e-02 2.176e-02 3.890e+01 0.793 0.4326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 g1:RT_
## group1 -0.290
## RT_z -0.023 0.008
## reps1 -0.223 0.074 0.109
## shockCond1 -0.026 0.025 -0.014 -0.007
## group1:RT_z 0.008 -0.023 -0.244 -0.029 0.014
## group1:rps1 0.075 -0.223 -0.028 -0.254 -0.022 0.109# explore interaction of group * rt
summary(lmer(scale(DAN_lh_sig_z) ~ RT_z + reps + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC%>% filter(group == 'control')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(DAN_lh_sig_z) ~ RT_z + reps + shockCond + (-1 + reps |
## subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC %>% filter(group == "control")
##
## REML criterion at convergence: 5058.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8479 -0.5884 0.0218 0.6535 3.8557
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.000e+00 0.000e+00
## reps4 3.864e-15 6.216e-08 NaN
## subid.1 RT_z 1.630e-03 4.037e-02
## subid.2 (Intercept) 0.000e+00 0.000e+00
## Residual 9.687e-01 9.842e-01
## Number of obs: 1795, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -9.165e-03 2.361e-02 1.778e+03 -0.388 0.6979
## RT_z 1.766e-01 2.524e-02 2.270e+01 6.995 4.28e-07 ***
## reps1 5.189e-02 2.388e-02 1.782e+03 2.173 0.0299 *
## shockCond1 8.016e-03 2.326e-02 1.790e+03 0.345 0.7304
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1
## RT_z -0.023
## reps1 -0.177 0.129
## shockCond1 -0.002 -0.001 -0.030summary(lmer(scale(DAN_lh_sig_z) ~ RT_z + reps + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC%>% filter(group == 'stress'),
control=lmerControl(optimizer="Nelder_Mead",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(DAN_lh_sig_z) ~ RT_z + reps + shockCond + (-1 + reps |
## subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC %>% filter(group == "stress")
## Control:
## lmerControl(optimizer = "Nelder_Mead", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 2853.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1480 -0.6158 -0.0349 0.6468 3.2952
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.025136 0.15854
## reps4 0.009454 0.09723 -1.00
## subid.1 RT_z 0.009058 0.09517
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.976237 0.98805
## Number of obs: 1002, groups: subid, 20
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004434 0.033099 122.800000 -0.134 0.894
## RT_z 0.065168 0.039129 15.000000 1.665 0.117
## reps1 0.018851 0.044510 15.900000 0.424 0.678
## shockCond1 -0.029215 0.031515 993.600000 -0.927 0.354
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1
## RT_z -0.023
## reps1 -0.317 0.082
## shockCond1 -0.054 -0.027 0.019# double check simple effects
# contrasts(dCleanStandardized_HC$group) = c(0,1)
# summary(lmer(scale(DAN_lh_sig_z) ~ group * (RT_z + reps) + shockCond +
# (-1 + reps|subid)+
# (-1 + RT_z|subid)+
# (1 |subid),
# data=dCleanStandardized_HC))
# contrasts(dCleanStandardized_HC$group) = c(1,0)
# summary(lmer(scale(DAN_lh_sig_z) ~ group * (RT_z + reps) + shockCond +
# (-1 + reps|subid)+
# (-1 + RT_z|subid)+
# (1 |subid),
# data=dCleanStandardized_HC))
# contrasts(dCleanStandardized_HC$group) = c(1,-1)
# interaction between shockcond and RT on DAN? in stress group
# remove subjs w/trial counts/bin < 6
dCleanStandardized_HC %>% filter(group == 'stress') %>%
group_by(subid, shockCond) %>%
summarise(count=n()) %>%
ungroup() %>%
mutate(subid = factor(subid)) %>%
complete(subid, shockCond, fill = list(count= 0)) %>% filter(count < 6)## # A tibble: 1 x 3
## subid shockCond count
## <fctr> <fctr> <dbl>
## 1 ap173 threat 1# remove slopes to converge
summary(lmer(scale(DAN_lh_sig_z) ~ (RT_z + reps) + shockCond +
shockCond:RT_z +
(-1 + RT_z|subid)+
(-1 + shockCond|subid)+
(1 |subid),
control=lmerControl(optimizer="Nelder_Mead",
optCtrl=list(maxfun=2e5)),
data=dCleanStandardized_HC %>% filter(group == 'stress',
subid != 'ap173')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## scale(DAN_lh_sig_z) ~ (RT_z + reps) + shockCond + shockCond:RT_z +
## (-1 + RT_z | subid) + (-1 + shockCond | subid) + (1 | subid)
## Data: dCleanStandardized_HC %>% filter(group == "stress", subid !=
## "ap173")
## Control:
## lmerControl(optimizer = "Nelder_Mead", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 2839.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2286 -0.5898 -0.0320 0.6266 3.2936
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid RT_z 7.996e-03 8.942e-02
## subid.1 shockCondsafe 0.000e+00 0.000e+00
## shockCondthreat 1.242e-12 1.114e-06 NaN
## subid.2 (Intercept) 0.000e+00 0.000e+00
## Residual 9.923e-01 9.962e-01
## Number of obs: 994, groups: subid, 19
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.002628 0.032427 971.700000 -0.081 0.935
## RT_z 0.057636 0.038627 14.800000 1.492 0.157
## reps1 0.018513 0.032721 988.500000 0.566 0.572
## shockCond1 -0.027980 0.031762 989.000000 -0.881 0.379
## RT_z:shockCond1 -0.003997 0.032159 854.300000 -0.124 0.901
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1 shckC1
## RT_z -0.020
## reps1 -0.219 0.103
## shockCond1 -0.047 -0.024 0.016
## RT_z:shckC1 -0.035 -0.033 0.025 0.006# is the RT * rep interaction significant?
dCleanStandardized_HC %>%
group_by(subid, reps) %>%
summarise(count=n()) %>%
ungroup() %>%
mutate(subid = factor(subid)) %>%
complete(subid, reps, fill = list(count= 0)) %>% filter(count < 6)## # A tibble: 3 x 3
## subid reps count
## <fctr> <fctr> <dbl>
## 1 ap110 2 3
## 2 ap158 2 2
## 3 ap173 2 2summary(lmer(scale(DAN_lh_sig_z) ~ group * (RT_z * reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(-1 + RT_z:reps|subid)+
(1 |subid),
data=dCleanStandardized_HC %>%
filter(!subid %in% c('ap110', 'ap158', 'ap173'))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(DAN_lh_sig_z) ~ group * (RT_z * reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (-1 + RT_z:reps | subid) +
## (1 | subid)
## Data: dCleanStandardized_HC %>% filter(!subid %in% c("ap110", "ap158",
## "ap173"))
##
## REML criterion at convergence: 7823.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1900 -0.5953 -0.0078 0.6481 3.8411
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 3.263e-03 5.712e-02
## reps4 1.522e-03 3.901e-02 -1.00
## subid.1 RT_z 4.573e-10 2.138e-05
## subid.2 RT_z:reps2 2.334e-03 4.831e-02
## RT_z:reps4 3.249e-03 5.700e-02 1.00
## subid.3 (Intercept) 0.000e+00 0.000e+00
## Residual 9.781e-01 9.890e-01
## Number of obs: 2758, groups: subid, 39
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.735e-03 2.030e-02 1.268e+03 -0.282 0.7776
## group1 -2.537e-03 2.030e-02 1.267e+03 -0.125 0.9006
## RT_z 1.127e-01 2.189e-02 4.130e+01 5.147 6.86e-06 ***
## reps1 3.540e-02 2.184e-02 3.880e+01 1.621 0.1131
## shockCond1 -4.999e-03 1.888e-02 2.742e+03 -0.265 0.7912
## RT_z:reps1 7.056e-03 2.003e-02 2.282e+03 0.352 0.7247
## group1:RT_z 5.530e-02 2.189e-02 4.130e+01 2.526 0.0154 *
## group1:reps1 1.606e-02 2.185e-02 3.880e+01 0.735 0.4666
## group1:RT_z:reps1 -2.893e-03 2.003e-02 2.282e+03 -0.144 0.8852
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 RT_z:1 gr1:RT_ grp1:1
## group1 -0.284
## RT_z -0.038 0.012
## reps1 -0.218 0.076 0.114
## shockCond1 -0.024 0.018 -0.013 -0.005
## RT_z:reps1 0.135 -0.028 -0.098 -0.036 0.009
## group1:RT_z 0.012 -0.038 -0.242 -0.027 0.015 0.040
## group1:rps1 0.076 -0.219 -0.027 -0.251 -0.020 0.013 0.114
## grp1:RT_z:1 -0.028 0.135 0.040 0.013 -0.018 -0.281 -0.098 -0.036Plot combined lh networks
## collapse
dm_avg = dm %>% filter(pcorr == 1) %>%
group_by(subid) %>%
mutate(rt_quintile = ntile(respRT, 5)) %>%
group_by(subid, group, rt_quintile) %>%
summarise(ccn = mean_(CCN_lh_sig),
dan = mean_(DAN_lh_sig),
count = n()) %>%
gather(key = 'network', value = 'signal', ccn:dan) %>%
mutate(network = replace(network, network=='ccn', 'CCN'),
network = replace(network, network=='dan', 'DAN'))
dfwc = summarySEwithin(dm_avg, measurevar="signal",
withinvars=c("rt_quintile", "network"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95)## Automatically converting the following non-factors to factors: rt_quintile, networkdfwc## group rt_quintile network N signal signal_norm sd
## 1 control 1 CCN 22 0.03230130 0.04816599 0.06275263
## 2 control 1 DAN 22 0.01262089 0.02848558 0.07062237
## 3 control 2 CCN 22 0.07582863 0.09169332 0.07688394
## 4 control 2 DAN 22 0.04543842 0.06130311 0.06851596
## 5 control 3 CCN 22 0.14757958 0.16344426 0.09290254
## 6 control 3 DAN 22 0.10225941 0.11812409 0.08514998
## 7 control 4 CCN 22 0.14597583 0.16184051 0.10035884
## 8 control 4 DAN 22 0.10304145 0.11890614 0.08069073
## 9 control 5 CCN 22 0.19266350 0.20852819 0.07902218
## 10 control 5 DAN 22 0.15007843 0.16594312 0.06707180
## 11 stress 1 CCN 20 0.13589630 0.11844515 0.14677943
## 12 stress 1 DAN 20 0.09766938 0.08021822 0.11519604
## 13 stress 2 CCN 20 0.10776132 0.09031017 0.11690720
## 14 stress 2 DAN 20 0.07138553 0.05393438 0.09810813
## 15 stress 3 CCN 20 0.14729661 0.12984546 0.10832576
## 16 stress 3 DAN 20 0.12303627 0.10558512 0.12015161
## 17 stress 4 CCN 20 0.20514167 0.18769051 0.11972469
## 18 stress 4 DAN 20 0.14671511 0.12926396 0.12196308
## 19 stress 5 CCN 20 0.16806054 0.15060939 0.08556226
## 20 stress 5 DAN 20 0.13798308 0.12053193 0.11675133
## se ci
## 1 0.01337891 0.02782296
## 2 0.01505674 0.03131220
## 3 0.01639171 0.03408843
## 4 0.01460765 0.03037828
## 5 0.01980689 0.04119068
## 6 0.01815404 0.03775338
## 7 0.02139658 0.04449662
## 8 0.01720332 0.03577626
## 9 0.01684759 0.03503647
## 10 0.01429976 0.02973797
## 11 0.03282088 0.06869489
## 12 0.02575862 0.05391341
## 13 0.02614125 0.05471426
## 14 0.02193764 0.04591602
## 15 0.02422238 0.05069802
## 16 0.02686672 0.05623269
## 17 0.02677126 0.05603288
## 18 0.02727177 0.05708048
## 19 0.01913230 0.04004437
## 20 0.02610639 0.05464130p2 = ggplot(dfwc, aes(x=rt_quintile, y=signal, group=group, color=group)) +
facet_grid(.~network)+
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=signal-se,
ymax=signal+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('Percent signal change')+
xlab('RT (quintiles)')+
ylim(-.015, .235)+
scale_color_manual(values=palette, guide = guide_legend(title = NULL))
ggsave(p2,
filename ='~/Experiments/AP/figs/respRTXfrontoparietalLH_quintile_HCrecollection.jpg',
dpi=150, width = 9, height=4)
ggsave(p2,
filename ='~/Dropbox/Stanford/Papers/Dissertation/Figures_defense/AP_respRTXfrontoparietalLH_quintile_HCrecollection.jpg',
dpi=300, width = 9, height=4)
p1 = ggplot(dfwc %>% filter(group == 'control'), aes(x=rt_quintile, y=signal, group=group, color=group)) +
facet_grid(.~network)+
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=signal-se,
ymax=signal+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('Percent signal change')+
xlab('RT (quintiles)')+
ylim(-.015, .235)+
scale_color_manual(values=palette, guide = guide_legend(title = NULL))
p1
ggsave(p1,
filename ='~/Dropbox/Stanford/Papers/Dissertation/Figures_defense/AP_respRTXfrontoparietalLH_quintile_HCrecollection_controlonly.jpg',
dpi=300, width = 9, height=4)
p2
## plot stress by shockCond
dm_avg = dm %>% filter(pcorr == 1,
group == 'stress',
subid != 'ap173') %>%
group_by(subid) %>%
mutate(rt_quintile = ntile(respRT, 5)) %>%
group_by(subid, shockCond, rt_quintile) %>%
summarise(ccn = mean_(CCN_lh_sig),
dan = mean_(DAN_lh_sig),
count = n()) %>%
gather(key = 'network', value = 'signal', ccn:dan) %>%
mutate(network = replace(network, network=='ccn', 'CCN'),
network = replace(network, network=='dan', 'DAN'))
dm_avg## Source: local data frame [378 x 6]
## Groups: subid, shockCond [38]
##
## # A tibble: 378 x 6
## subid shockCond rt_quintile count network signal
## <fctr> <fctr> <int> <int> <chr> <dbl>
## 1 ap150 safe 1 6 CCN 0.12465318
## 2 ap150 safe 2 6 CCN -0.04759280
## 3 ap150 safe 3 7 CCN 0.11967741
## 4 ap150 safe 4 8 CCN 0.34404922
## 5 ap150 safe 5 6 CCN 0.47773393
## 6 ap150 threat 1 8 CCN 0.36193132
## 7 ap150 threat 2 7 CCN 0.16074072
## 8 ap150 threat 3 6 CCN -0.10565980
## 9 ap150 threat 4 5 CCN 0.07337990
## 10 ap150 threat 5 7 CCN -0.01907539
## # ... with 368 more rowsdfwc = summarySEwithin(dm_avg, measurevar="signal",
withinvars=c("rt_quintile", "network", "shockCond"),
idvar="subid", na.rm=TRUE, conf.interval=.95)## Automatically converting the following non-factors to factors: rt_quintile, networkdfwc## rt_quintile network shockCond N signal signal_norm sd
## 1 1 CCN safe 19 0.10636532 0.10629496 0.1950704
## 2 1 CCN threat 19 0.11400598 0.11393562 0.1486527
## 3 1 DAN safe 19 0.06431018 0.06423982 0.1259749
## 4 1 DAN threat 19 0.14225934 0.14218898 0.1735881
## 5 2 CCN safe 19 0.16436471 0.16429435 0.1815611
## 6 2 CCN threat 19 0.07176070 0.07169034 0.1546956
## 7 2 DAN safe 19 0.14215485 0.14208449 0.1525689
## 8 2 DAN threat 19 0.02219062 0.02212026 0.1474942
## 9 3 CCN safe 19 0.15451356 0.15444319 0.1619787
## 10 3 CCN threat 18 0.18592459 0.18659303 0.1588574
## 11 3 DAN safe 19 0.11500784 0.11493748 0.2240081
## 12 3 DAN threat 18 0.16257757 0.16324602 0.1833754
## 13 4 CCN safe 19 0.22755111 0.22748075 0.1641223
## 14 4 CCN threat 19 0.16997784 0.16990748 0.1826248
## 15 4 DAN safe 19 0.13650646 0.13643609 0.1844395
## 16 4 DAN threat 19 0.14512711 0.14505675 0.1921394
## 17 5 CCN safe 19 0.19027665 0.19020629 0.1812135
## 18 5 CCN threat 19 0.17079781 0.17072744 0.1618633
## 19 5 DAN safe 19 0.13209532 0.13202496 0.2193852
## 20 5 DAN threat 19 0.15853562 0.15846526 0.1634688
## se ci
## 1 0.04475221 0.09402091
## 2 0.03410327 0.07164832
## 3 0.02890063 0.06071798
## 4 0.03982384 0.08366678
## 5 0.04165298 0.08750966
## 6 0.03548961 0.07456091
## 7 0.03500170 0.07353585
## 8 0.03383749 0.07108993
## 9 0.03716046 0.07807122
## 10 0.03744305 0.07899792
## 11 0.05139098 0.10796845
## 12 0.04322201 0.09119046
## 13 0.03765223 0.07910439
## 14 0.04189700 0.08802233
## 15 0.04231331 0.08889697
## 16 0.04407980 0.09260823
## 17 0.04157322 0.08734209
## 18 0.03713399 0.07801561
## 19 0.05033042 0.10574030
## 20 0.03750231 0.07878944p2 = ggplot(dfwc, aes(x=rt_quintile, y=signal, group=shockCond, color=shockCond)) +
facet_grid(.~network)+
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=signal-se,
ymax=signal+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('Percent signal change')+
xlab('RT (quintiles)')+
scale_color_manual(values=c('mediumpurple', 'darkorange'),
guide = guide_legend(title = NULL))
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/stress_respRTXfrontoparietalLH_quintile_HCrecollection.jpg',
dpi=150, width = 9, height=4)
p2
Look at lh CCN raw timecourse
Compare with misses first, just to make sure we’re getting good signal for HC assoc hits
ccn_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-frontoparietal.csv')
# first, compare with misses to make sure we're getting good signal for CCN:
# merge w/data (filter artifacts + get extra trial info)
ccn_lh = ccn_lh %>% filter(condition %in% c('sourcehit', 'M')) %>%
# inner join w/d, where have a new col indicating median of RT for that condition
inner_join(d %>% group_by(subid, cond) %>% mutate(rt_bin = ntile(respRT, 3)) %>% ungroup(),
by=c('subid', 'run', 'onset')) %>%
mutate(subid = factor(subid),
cond = factor(cond))
with(ccn_lh, table(cond, condition.x))## condition.x
## cond CR FA itemhit_lo M sourcehit sourcemiss_hi
## M 0 0 0 7252 0 0
## sourcehit 0 0 0 0 20489 0ggplot(ccn_lh %>% group_by(subid, cond, time) %>%
summarise(ccn_sig = mean(mean_activity)) %>%
group_by(time, cond) %>%
summarise(mean(ccn_sig)),
aes(x=time, y=`mean(ccn_sig)`, color=cond, group=cond)) +
geom_line()
Break down by RT
ccn_lh = ccn_lh %>% filter(cond == 'sourcehit')
# double check that split of RT is good
ccn_lh %>% filter(time == 0) %>% group_by(subid, group, rt_bin) %>%
summarise(mean(respRT), max(respRT), min(respRT))## Source: local data frame [132 x 6]
## Groups: subid, group [?]
##
## # A tibble: 132 x 6
## subid group rt_bin `mean(respRT)` `max(respRT)` `min(respRT)`
## <fctr> <fctr> <int> <dbl> <dbl> <dbl>
## 1 ap100 control 1 1.472169 1.8256 1.1028
## 2 ap100 control 2 2.091476 2.3408 1.8843
## 3 ap100 control 3 2.902400 3.9149 2.3666
## 4 ap101 control 1 1.524176 1.7988 1.1682
## 5 ap101 control 2 1.961079 2.1640 1.8012
## 6 ap101 control 3 2.655738 3.7207 2.2345
## 7 ap102 control 1 1.563682 1.7736 1.1019
## 8 ap102 control 2 1.993450 2.2263 1.7762
## 9 ap102 control 3 2.730459 3.7812 2.2745
## 10 ap103 control 1 2.292159 2.7029 1.6285
## # ... with 122 more rowsccn_avg = ccn_lh %>%
group_by(subid, group, rt_bin, time) %>%
summarise(ccn_sig = mean(mean_activity)) %>%
group_by(group, rt_bin, time) %>%
summarise(mean = mean(ccn_sig), se = std.error(ccn_sig), n())
ccn_avg ## Source: local data frame [42 x 6]
## Groups: group, rt_bin [?]
##
## # A tibble: 42 x 6
## group rt_bin time mean se `n()`
## <fctr> <int> <int> <dbl> <dbl> <int>
## 1 control 1 0 0.037762404 0.02027021 22
## 2 control 1 2 -0.041527517 0.02286603 22
## 3 control 1 4 0.018019148 0.02060081 22
## 4 control 1 6 0.103588084 0.01943584 22
## 5 control 1 8 0.086801488 0.02100314 22
## 6 control 1 10 -0.031153485 0.02074640 22
## 7 control 1 12 -0.123887369 0.02401769 22
## 8 control 2 0 -0.008986119 0.02484918 22
## 9 control 2 2 -0.071288820 0.02342123 22
## 10 control 2 4 0.015258543 0.02030362 22
## # ... with 32 more rowsblues <- brewer.pal(4, "Blues")[2:4]
p2 = ggplot(ccn_avg, aes(x=factor(time), y=mean, group=factor(rt_bin), color=factor(rt_bin))) +
facet_grid(.~group) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=mean-se,
ymax=mean+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('CCN activity (% change)')+
xlab('Time post-stimulus onset (s)') +
guides(color=guide_legend(title="RT (tertile)")) +
scale_color_manual(values=blues)
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/CCNlh_bytimeXrttertile_HCrecollection.jpg',
dpi=150, width = 9, height=4)
p2
For comparison, take a look at posterior hippocampal ROI
Does this peak earlier, and CCN kicks in post-retrieval for evaluation? Or, is it extended (retrieving more memories)?
Compare with misses first, just to make sure we’re getting good signal for HC assoc hits
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-hippocampus-tail.csv')
roi_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-hippocampus-tail.csv')
# Collapse across hemispheres
ccn_lh = bind_rows("lh" = roi_lh, "rh" = roi_rh, .id = "hemi") %>%
filter(condition %in% c('sourcehit', 'M')) %>%
group_by(subid, hemi, run, onset, time) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset, time) %>%
summarise(mean_activity = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characterccn_lh## # A tibble: 28,378 x 5
## subid run onset time mean_activity
## <fctr> <int> <dbl> <int> <dbl>
## 1 ap100 1 19.8642 0 0.7336617
## 2 ap100 1 19.8642 2 0.6086769
## 3 ap100 1 19.8642 4 0.7270279
## 4 ap100 1 19.8642 6 0.7332039
## 5 ap100 1 19.8642 8 0.3253021
## 6 ap100 1 19.8642 10 0.1845512
## 7 ap100 1 19.8642 12 0.4814529
## 8 ap100 1 29.3132 0 0.1845512
## 9 ap100 1 29.3132 2 0.4814529
## 10 ap100 1 29.3132 4 0.3145866
## # ... with 28,368 more rows# first, compare with misses to make sure we're getting good signal
# merge w/data (filter artifacts + get extra trial info)
ccn_lh = ccn_lh %>%
# inner join w/d, where have a new col indicating median of RT for that condition
inner_join(d %>% group_by(subid, cond) %>% mutate(rt_bin = ntile(respRT, 3)) %>% ungroup(),
by=c('subid', 'run', 'onset')) %>%
mutate(subid = factor(subid),
cond = factor(cond))
ggplot(ccn_lh %>% group_by(group, subid, cond, time) %>%
summarise(ccn_sig = mean(mean_activity)) %>%
group_by(group, time, cond) %>%
summarise(mean(ccn_sig)),
aes(x=time, y=`mean(ccn_sig)`, color=cond, group=cond)) +
facet_grid(.~group)+
geom_line()
fit = lmer(scale(hipp_tail_sig_z) ~ group * (RT_z + reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC)
summary(fit)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(hipp_tail_sig_z) ~ group * (RT_z + reps) + shockCond +
## (-1 + reps | subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC
##
## REML criterion at convergence: 7969.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6861 -0.5859 0.0264 0.6555 3.0920
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.006291 0.07932
## reps4 0.002945 0.05427 -1.00
## subid.1 RT_z 0.002003 0.04475
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.993705 0.99685
## Number of obs: 2797, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.017e-03 2.026e-02 8.944e+02 -0.198 0.843
## group1 -4.312e-03 2.026e-02 8.929e+02 -0.213 0.831
## RT_z 2.628e-02 2.128e-02 3.560e+01 1.235 0.225
## reps1 2.165e-02 2.318e-02 4.040e+01 0.934 0.356
## shockCond1 4.847e-03 1.890e-02 2.781e+03 0.256 0.798
## group1:RT_z 2.151e-02 2.128e-02 3.560e+01 1.011 0.319
## group1:reps1 2.749e-02 2.318e-02 4.040e+01 1.186 0.243
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 g1:RT_
## group1 -0.289
## RT_z -0.024 0.009
## reps1 -0.233 0.071 0.106
## shockCond1 -0.026 0.025 -0.015 -0.006
## group1:RT_z 0.009 -0.024 -0.256 -0.029 0.014
## group1:rps1 0.072 -0.234 -0.028 -0.234 -0.022 0.106## collapse
dm_avg = dm %>% filter(pcorr == 1) %>%
group_by(subid) %>%
mutate(rt_quintile = ntile(respRT, 5)) %>%
group_by(subid, group, rt_quintile) %>%
summarise(ccn = mean_(hipp_tail_sig),
count = n())
dfwc = summarySEwithin(dm_avg, measurevar="ccn",
withinvars=c("rt_quintile"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95)## Automatically converting the following non-factors to factors: rt_quintiledfwc## group rt_quintile N ccn ccn_norm sd se
## 1 control 1 22 0.03583481 0.03440040 0.07291690 0.01554594
## 2 control 2 22 0.05225329 0.05081887 0.07497496 0.01598472
## 3 control 3 22 0.10119444 0.09976003 0.08017759 0.01709392
## 4 control 4 22 0.06906541 0.06763099 0.06650354 0.01417860
## 5 control 5 22 0.09192640 0.09049198 0.06876062 0.01465981
## 6 stress 1 20 0.08231061 0.08388847 0.10191196 0.02278821
## 7 stress 2 20 0.03821644 0.03979430 0.08003474 0.01789631
## 8 stress 3 20 0.03674231 0.03832017 0.10720746 0.02397232
## 9 stress 4 20 0.11109159 0.11266945 0.12577792 0.02812480
## 10 stress 5 20 0.06685203 0.06842989 0.07795019 0.01743019
## ci
## 1 0.03232954
## 2 0.03324204
## 3 0.03554875
## 4 0.02948602
## 5 0.03048675
## 6 0.04769627
## 7 0.03745741
## 8 0.05017464
## 9 0.05886588
## 10 0.03648181p2 = ggplot(dfwc, aes(x=rt_quintile, y=ccn, group=group, color=group)) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=ccn-se,
ymax=ccn+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('Percent signal change')+
xlab('RT (quintiles)')+
scale_color_manual(values=palette, guide = guide_legend(title = NULL))
p2
ggsave(p2,
filename ='~/Dropbox/Stanford/Papers/Dissertation/Figures_defense/AP_respRTXhippTail_quintile_HCrecollection.jpg',
dpi=300, width = 6, height=4)Break down by RT
# subtract out baseline for each trial, as this is a little variable across people (potentially due to # of SHs across groups?)
ccn_avg = ccn_lh %>% mutate(time = paste0('T',time)) %>%
spread(key = time, value=mean_activity) %>%
mutate(T2 = T2-T0,
T4 = T4 - T0,
T6 = T6 - T0,
T8 = T8 - T0,
T10 = T10 - T0,
T12 = T12 - T0) %>%
mutate(T0 = 0) %>%
gather(time, mean_activity, T0:T8) %>%
mutate(time = as.numeric(substr(time, 2,3))) %>%
dplyr::select(subid, group, onset, cond, time, mean_activity, everything()) %>%
mutate(rt_bin=replace(rt_bin, cond=='M', 1)) %>%
group_by(subid, group, cond, time, rt_bin) %>%
summarise(ccn_sig = mean(mean_activity)) %>%
group_by(group, time, cond, rt_bin) %>%
summarise(mean = mean(ccn_sig), se = std.error(ccn_sig), n()) %>%
mutate(cond_rt = paste0(cond, '-', rt_bin),
cond_rt = replace(cond_rt, cond_rt == 'M-1', 'M'))
ccn_avg## Source: local data frame [56 x 8]
## Groups: group, time, cond [28]
##
## # A tibble: 56 x 8
## group time cond rt_bin mean se `n()`
## <fctr> <dbl> <fctr> <dbl> <dbl> <dbl> <int>
## 1 control 0 M 1 0.000000000 0.00000000 22
## 2 control 0 sourcehit 1 0.000000000 0.00000000 22
## 3 control 0 sourcehit 2 0.000000000 0.00000000 22
## 4 control 0 sourcehit 3 0.000000000 0.00000000 22
## 5 control 2 M 1 -0.020574439 0.01726235 22
## 6 control 2 sourcehit 1 -0.023227816 0.01954083 22
## 7 control 2 sourcehit 2 0.004117345 0.02226814 22
## 8 control 2 sourcehit 3 -0.026347231 0.02180056 22
## 9 control 4 M 1 -0.030093323 0.03648434 22
## 10 control 4 sourcehit 1 0.077489085 0.02676543 22
## # ... with 46 more rows, and 1 more variables: cond_rt <chr>blues <- brewer.pal(4, "Blues")[2:4]
reds = brewer.pal(4, "Reds")[3]
colorpal = c(reds, blues)
p2 = ggplot(ccn_avg, aes(x=factor(time), y=mean, color=cond_rt, group=cond_rt)) + #group=factor(rt_bin), color=factor(rt_bin))) +
facet_grid(.~group) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=mean-se,
ymax=mean+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('Hipp tail activity\n(% change rel onset)')+
xlab('Time post-stimulus onset (s)') +
guides(color=guide_legend(title="RT (tertile)")) +
scale_color_manual(values=colorpal)
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/hippTail_bytimeXrttertile_HCrecollection_substractBL.jpg',
dpi=150, width = 9, height=4)
p2
Bilateral networks
# bilateral now
summary(lmer(scale(CCN_sig_z) ~ group * (RT_z + reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(CCN_sig_z) ~ group * (RT_z + reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC
##
## REML criterion at convergence: 7936.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9333 -0.6212 0.0213 0.6472 3.6626
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.000e+00 0.000e+00
## reps4 2.714e-15 5.210e-08 NaN
## subid.1 RT_z 3.667e-03 6.055e-02
## subid.2 (Intercept) 0.000e+00 0.000e+00
## Residual 9.841e-01 9.920e-01
## Number of obs: 2797, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.777e-03 1.999e-02 2.752e+03 -0.239 0.811196
## group1 -6.004e-03 1.999e-02 2.752e+03 -0.300 0.763983
## RT_z 8.368e-02 2.221e-02 3.230e+01 3.768 0.000663 ***
## reps1 2.669e-02 2.020e-02 2.788e+03 1.321 0.186499
## shockCond1 8.952e-03 1.880e-02 2.789e+03 0.476 0.634043
## group1:RT_z 5.985e-02 2.221e-02 3.230e+01 2.695 0.011084 *
## group1:reps1 3.438e-02 2.020e-02 2.788e+03 1.702 0.088883 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 g1:RT_
## group1 -0.292
## RT_z -0.023 0.008
## reps1 -0.204 0.078 0.116
## shockCond1 -0.026 0.024 -0.014 -0.008
## group1:RT_z 0.008 -0.023 -0.237 -0.029 0.013
## group1:rps1 0.079 -0.205 -0.029 -0.288 -0.021 0.115# interaction
summary(lmer(scale(CCN_sig_z) ~ (RT_z + reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC %>% filter(group == 'control')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## scale(CCN_sig_z) ~ (RT_z + reps) + shockCond + (-1 + reps | subid) +
## (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC %>% filter(group == "control")
##
## REML criterion at convergence: 5075.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9481 -0.6062 0.0407 0.6464 3.6747
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.000e+00 0.000e+00
## reps4 5.920e-25 7.694e-13 NaN
## subid.1 RT_z 0.000e+00 0.000e+00
## subid.2 (Intercept) 0.000e+00 0.000e+00
## Residual 9.794e-01 9.897e-01
## Number of obs: 1795, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.049e-02 2.373e-02 1.791e+03 -0.442 0.6586
## RT_z 1.453e-01 2.357e-02 1.791e+03 6.165 8.67e-10 ***
## reps1 5.975e-02 2.398e-02 1.791e+03 2.491 0.0128 *
## shockCond1 7.280e-04 2.337e-02 1.791e+03 0.031 0.9752
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1
## RT_z -0.025
## reps1 -0.177 0.142
## shockCond1 -0.002 -0.002 -0.029summary(lmer(scale(CCN_sig_z) ~ (RT_z + reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC %>% filter(group == 'stress')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## scale(CCN_sig_z) ~ (RT_z + reps) + shockCond + (-1 + reps | subid) +
## (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC %>% filter(group == "stress")
##
## REML criterion at convergence: 2857.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9415 -0.6605 -0.0054 0.6354 3.2161
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.010666 0.10328
## reps4 0.004261 0.06527 -1.00
## subid.1 RT_z 0.014358 0.11982
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.982433 0.99118
## Number of obs: 1002, groups: subid, 20
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.435e-04 3.261e-02 2.408e+02 -0.004 0.996
## RT_z 2.331e-02 4.271e-02 1.580e+01 0.546 0.593
## reps1 -6.058e-03 3.841e-02 1.640e+01 -0.158 0.877
## shockCond1 2.581e-02 3.161e-02 9.957e+02 0.817 0.414
##
## Correlation of Fixed Effects:
## (Intr) RT_z reps1
## RT_z -0.021
## reps1 -0.266 0.086
## shockCond1 -0.054 -0.025 0.019# Now, double check out simple effects
# contrasts(dCleanStandardized_HC$group) = c(0,1)
# summary(lmer(scale(CCN_sig_z) ~ group * (RT_z + reps) + shockCond +
# (-1 + reps|subid)+
# (-1 + RT_z|subid)+
# (1 |subid),
# data=dCleanStandardized_HC))
#
# contrasts(dCleanStandardized_HC$group) = c(1,0)
# summary(lmer(scale(CCN_sig_z) ~ group * (RT_z + reps) + shockCond +
# (-1 + reps|subid)+
# (-1 + RT_z|subid)+
# (1 |subid),
# data=dCleanStandardized_HC))
# contrasts(dCleanStandardized_HC$group) = c(1,-1)
# is the RT * rep interaction significant?
summary(lmer(scale(CCN_sig_z) ~ group * (RT_z * reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(-1 + RT_z:reps|subid)+
(1 |subid),
data=dCleanStandardized_HC))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(CCN_sig_z) ~ group * (RT_z * reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (-1 + RT_z:reps | subid) +
## (1 | subid)
## Data: dCleanStandardized_HC
##
## REML criterion at convergence: 7946.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9284 -0.6236 0.0204 0.6434 3.6851
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.000e+00 0.000e+00
## reps4 2.915e-17 5.399e-09 NaN
## subid.1 RT_z 0.000e+00 0.000e+00
## subid.2 RT_z:reps2 5.751e-03 7.583e-02
## RT_z:reps4 2.459e-03 4.959e-02 1.00
## subid.3 (Intercept) 0.000e+00 0.000e+00
## Residual 9.838e-01 9.919e-01
## Number of obs: 2797, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.070e-03 2.018e-02 2.752e+03 -0.350 0.726111
## group1 -9.342e-03 2.018e-02 2.752e+03 -0.463 0.643424
## RT_z 8.510e-02 2.258e-02 2.960e+01 3.770 0.000728 ***
## reps1 2.721e-02 2.021e-02 2.786e+03 1.346 0.178327
## shockCond1 9.189e-03 1.880e-02 2.787e+03 0.489 0.625141
## RT_z:reps1 -1.361e-02 2.019e-02 3.638e+02 -0.674 0.500754
## group1:RT_z 6.266e-02 2.258e-02 2.960e+01 2.775 0.009465 **
## group1:reps1 3.528e-02 2.022e-02 2.786e+03 1.745 0.081099 .
## group1:RT_z:reps1 -2.239e-02 2.019e-02 3.637e+02 -1.109 0.268047
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 RT_z:1 gr1:RT_ grp1:1
## group1 -0.290
## RT_z -0.039 0.013
## reps1 -0.209 0.079 0.118
## shockCond1 -0.024 0.022 -0.015 -0.008
## RT_z:reps1 0.133 -0.031 -0.172 -0.039 0.009
## group1:RT_z 0.013 -0.039 -0.237 -0.031 0.016 0.043
## group1:rps1 0.080 -0.209 -0.030 -0.288 -0.020 0.015 0.118
## grp1:RT_z:1 -0.031 0.133 0.043 0.015 -0.017 -0.283 -0.172 -0.039## DAN
summary(lmer(scale(DAN_sig_z) ~ group * (RT_z + reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(1 |subid),
data=dCleanStandardized_HC))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(DAN_sig_z) ~ group * (RT_z + reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (1 | subid)
## Data: dCleanStandardized_HC
##
## REML criterion at convergence: 7938.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0028 -0.6091 0.0026 0.6594 3.6182
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.006122 0.07824
## reps4 0.002809 0.05300 -1.00
## subid.1 RT_z 0.003788 0.06155
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.981210 0.99056
## Number of obs: 2797, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.639e-03 2.013e-02 8.730e+02 -0.429 0.667941
## group1 -2.298e-03 2.013e-02 8.715e+02 -0.114 0.909156
## RT_z 9.146e-02 2.230e-02 3.850e+01 4.102 0.000205 ***
## reps1 4.282e-02 2.299e-02 3.800e+01 1.863 0.070252 .
## shockCond1 -3.920e-03 1.879e-02 2.780e+03 -0.209 0.834801
## group1:RT_z 4.412e-02 2.230e-02 3.850e+01 1.979 0.055024 .
## group1:reps1 1.641e-02 2.299e-02 3.800e+01 0.714 0.479710
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 g1:RT_
## group1 -0.289
## RT_z -0.023 0.009
## reps1 -0.234 0.071 0.101
## shockCond1 -0.026 0.025 -0.014 -0.006
## group1:RT_z 0.009 -0.023 -0.236 -0.028 0.014
## group1:rps1 0.072 -0.234 -0.028 -0.235 -0.022 0.101# is the RT * rep interaction significant?
summary(lmer(scale(DAN_sig_z) ~ group * (RT_z * reps) + shockCond +
(-1 + reps|subid)+
(-1 + RT_z|subid)+
(-1 + RT_z:reps|subid)+
(1 |subid),
data=dCleanStandardized_HC))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: scale(DAN_sig_z) ~ group * (RT_z * reps) + shockCond + (-1 +
## reps | subid) + (-1 + RT_z | subid) + (-1 + RT_z:reps | subid) +
## (1 | subid)
## Data: dCleanStandardized_HC
##
## REML criterion at convergence: 7950.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9982 -0.6074 0.0035 0.6593 3.6166
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid reps2 0.006117 0.07821
## reps4 0.002805 0.05296 -1.00
## subid.1 RT_z 0.000000 0.00000
## subid.2 RT_z:reps2 0.002860 0.05348
## RT_z:reps4 0.004793 0.06923 1.00
## subid.3 (Intercept) 0.000000 0.00000
## Residual 0.981757 0.99084
## Number of obs: 2797, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.167e-03 2.032e-02 8.940e+02 -0.402 0.687880
## group1 -2.868e-03 2.032e-02 8.926e+02 -0.141 0.887796
## RT_z 9.098e-02 2.243e-02 3.990e+01 4.056 0.000225 ***
## reps1 4.265e-02 2.300e-02 3.810e+01 1.854 0.071458 .
## shockCond1 -3.923e-03 1.880e-02 2.778e+03 -0.209 0.834746
## RT_z:reps1 4.085e-03 2.002e-02 1.544e+03 0.204 0.838340
## group1:RT_z 4.457e-02 2.243e-02 3.990e+01 1.987 0.053806 .
## group1:reps1 1.661e-02 2.301e-02 3.810e+01 0.722 0.474805
## group1:RT_z:reps1 -3.922e-03 2.002e-02 1.544e+03 -0.196 0.844717
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 RT_z:1 gr1:RT_ grp1:1
## group1 -0.287
## RT_z -0.038 0.013
## reps1 -0.236 0.072 0.105
## shockCond1 -0.024 0.022 -0.015 -0.006
## RT_z:reps1 0.135 -0.031 -0.089 -0.035 0.009
## group1:RT_z 0.013 -0.038 -0.238 -0.030 0.015 0.038
## group1:rps1 0.073 -0.236 -0.029 -0.235 -0.021 0.013 0.105
## grp1:RT_z:1 -0.031 0.134 0.038 0.013 -0.018 -0.285 -0.089 -0.035Plot bilateral
## bilateral
dm_avg = dm %>% filter(pcorr == 1) %>%
group_by(subid) %>%
mutate(ccn_quintile = ntile(respRT, 5)) %>%
group_by(subid, group, ccn_quintile, reps) %>%
summarise(vtc_logit = mean_(CCN_sig), count = n()) %>%
group_by(subid, group, ccn_quintile, reps) %>%
summarise(vtc_logit = mean_(vtc_logit), count = n())%>%
ungroup() %>%
mutate(reps = revalue(reps, replace = c('2' = 'weak',
'4' = 'strong')))
dfwc = summarySEwithin(dm_avg, measurevar="vtc_logit",
withinvars=c("ccn_quintile", "reps"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95)## Automatically converting the following non-factors to factors: ccn_quintiledfwc## group ccn_quintile reps N vtc_logit vtc_logit_norm sd
## 1 control 1 strong 22 0.057717431 0.07249908 0.09518539
## 2 control 1 weak 22 -0.051096562 -0.03631491 0.15220464
## 3 control 2 strong 22 0.065354748 0.08013640 0.07541628
## 4 control 2 weak 21 -0.001121288 0.01861256 0.14394146
## 5 control 3 strong 22 0.099638216 0.11441987 0.07699884
## 6 control 3 weak 20 0.098151890 0.11819680 0.13219196
## 7 control 4 strong 22 0.110938241 0.12571989 0.12853080
## 8 control 4 weak 22 0.056486425 0.07126808 0.11659648
## 9 control 5 strong 22 0.141587106 0.15636876 0.08045122
## 10 control 5 weak 20 0.092709770 0.11275468 0.13905546
## 11 stress 1 strong 20 0.117170530 0.09809445 0.17023626
## 12 stress 1 weak 17 0.079307913 0.06574314 0.22387668
## 13 stress 2 strong 20 0.047921529 0.02884545 0.18467113
## 14 stress 2 weak 20 0.084315743 0.06523966 0.26591751
## 15 stress 3 strong 20 0.083656394 0.06458031 0.13875341
## 16 stress 3 weak 18 0.081814608 0.06657696 0.14436736
## 17 stress 4 strong 20 0.163103773 0.14402769 0.15854473
## 18 stress 4 weak 20 0.146195426 0.12711934 0.19845885
## 19 stress 5 strong 20 0.086047377 0.06697130 0.16413163
## 20 stress 5 weak 19 0.116791153 0.10010423 0.16072191
## se ci
## 1 0.02029359 0.04220284
## 2 0.03245014 0.06748376
## 3 0.01607880 0.03343770
## 4 0.03141060 0.06552137
## 5 0.01641621 0.03413937
## 6 0.02955902 0.06186774
## 7 0.02740286 0.05698736
## 8 0.02485845 0.05169599
## 9 0.01715226 0.03567007
## 10 0.03109375 0.06507996
## 11 0.03806599 0.07967302
## 12 0.05429807 0.11510677
## 13 0.04129372 0.08642875
## 14 0.05946096 0.12445323
## 15 0.03102621 0.06493860
## 16 0.03402771 0.07179220
## 17 0.03545168 0.07420122
## 18 0.04437675 0.09288160
## 19 0.03670095 0.07681597
## 20 0.03687214 0.07746548p2 = ggplot(dfwc, aes(x=ccn_quintile, y=vtc_logit, group=group, color=group)) +
facet_grid(.~reps) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=vtc_logit-se,
ymax=vtc_logit+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('CCN activity (% change)')+
xlab('RT (quintiles)')+
scale_color_manual(values=palette, guide = guide_legend(title = NULL))
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/respRTXccn_quintileXrep_HCrecollection.jpg',
dpi=150, width = 9, height=4)
p2
## collapse
dm_avg = dm %>% filter(pcorr == 1) %>%
group_by(subid) %>%
mutate(ccn_quintile = ntile(respRT, 5)) %>%
group_by(subid, group, ccn_quintile, reps) %>%
summarise(vtc_logit = mean_(CCN_sig), count = n()) %>%
group_by(subid, group, ccn_quintile) %>%
summarise(vtc_logit = mean_(vtc_logit), count = n())
dfwc = summarySEwithin(dm_avg, measurevar="vtc_logit",
withinvars=c("ccn_quintile"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95)## Automatically converting the following non-factors to factors: ccn_quintiledfwc## group ccn_quintile N vtc_logit vtc_logit_norm sd
## 1 control 1 22 0.003310434 0.01810233 0.07102882
## 2 control 2 22 0.034525988 0.04931788 0.09007522
## 3 control 3 22 0.098644374 0.11343626 0.09696698
## 4 control 4 22 0.083712333 0.09850422 0.08659460
## 5 control 5 22 0.126286356 0.14107825 0.08776426
## 6 stress 1 20 0.096224656 0.07995358 0.16420625
## 7 stress 2 20 0.066118636 0.04984756 0.14201432
## 8 stress 3 20 0.080476036 0.06420496 0.11156895
## 9 stress 4 20 0.154649599 0.13837852 0.14243421
## 10 stress 5 20 0.104325411 0.08805433 0.10384001
## se ci
## 1 0.01514340 0.03149241
## 2 0.01920410 0.03993711
## 3 0.02067343 0.04299275
## 4 0.01846203 0.03839389
## 5 0.01871140 0.03891249
## 6 0.03671763 0.07685089
## 7 0.03175537 0.06646475
## 8 0.02494758 0.05221588
## 9 0.03184926 0.06666126
## 10 0.02321933 0.04859862p2 = ggplot(dfwc, aes(x=ccn_quintile, y=vtc_logit, group=group, color=group)) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=vtc_logit-se,
ymax=vtc_logit+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('CCN activity (% change)')+
xlab('RT (quintiles)')+
scale_color_manual(values=palette, guide = guide_legend(title = NULL))
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/respRTXccn_quintile_HCrecollection.jpg',
dpi=150, width = 6, height=4)
p2
2b (iii): Explore: Is hipp/vtc greater for faster high confidence memories? Or for longer RT (potentially retrieval of more things to guide associate recall) memories?
Hipp ~ RT:
# look at trial counts/subj
with(dCleanStandardized_HC, table(subid))## subid
## ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ap110 ap111 ap113
## 87 102 66 111 146 117 122 28 58 20 73 75
## ap114 ap115 ap116 ap117 ap118 ap119 ap120 ap121 ap122 ap150 ap152 ap153
## 101 77 144 84 62 112 71 33 95 66 40 31
## ap154 ap155 ap157 ap158 ap159 ap160 ap161 ap162 ap163 ap164 ap165 ap166
## 42 38 51 11 50 51 27 41 107 81 64 71
## ap167 ap169 ap170 ap171 ap172 ap173
## 38 69 74 33 20 8data.frame(with(dCleanStandardized_HC, table(subid))) %>% summarise(mean(Freq), sd(Freq))## mean(Freq) sd(Freq)
## 1 66.59524 34.91387head(dCleanStandardized_HC)## Source: local data frame [6 x 87]
## Groups: subid [1]
##
## # A tibble: 6 x 87
## X.x run trial onset duration shockCond shockTrial target
## <int> <dbl> <int> <dbl> <dbl> <fctr> <int> <fctr>
## 1 2 1 3 19.8642 9.4224 safe 0 VIOLIN
## 2 3 1 4 29.3132 11.3389 safe 0 MEDAL
## 3 4 1 5 40.6786 9.7644 safe 0 MANURE
## 4 5 1 6 50.4701 9.1180 safe 0 SARDINE
## 5 14 1 15 137.3692 10.3760 safe 0 SEAL
## 6 15 1 16 147.7725 11.9447 safe 0 SCROLL
## # ... with 79 more variables: associate <fctr>, resp <fctr>, acc <fctr>,
## # accSpec <fctr>, respRT <dbl>, subid <fctr>, remove <lgl>,
## # anxious <int>, happy <int>, safe <int>, stressed <int>,
## # life_stress <int>, sleep <dbl>, cond_orig <fctr>,
## # shock_and_post <int>, conf <fctr>, onset_adj <dbl>,
## # mem_conditions <fctr>, obj_conditions <fctr>, onset_code <fctr>,
## # rt_z <dbl>, mem_conditions_byrep <fctr>, onset_code_byrep <fctr>,
## # img_type <fctr>, X.y <int>, index <int>, group <fctr>,
## # condition <fctr>, category <fctr>, X0 <dbl>, X2 <dbl>, X4 <dbl>,
## # X6 <dbl>, X8 <dbl>, X10 <dbl>, X12 <dbl>, avg_logit <dbl>,
## # cond <fctr>, reps <fctr>, pcorr <fctr>, vtc_logit <dbl>,
## # imgType <fctr>, hipp_sig <dbl>, hipp_tail_sig <dbl>, DAN_sig <dbl>,
## # CCN_sig <dbl>, rsp_sig <dbl>, angular_lh_sig <dbl>, DAN_lh_sig <dbl>,
## # CCN_lh_sig <dbl>, ang_logit <dbl>, hipp_logit <dbl>,
## # vtc_nophc_logit <dbl>, ang_logit_z <dbl>, vtc_logit_z <dbl>,
## # hipp_logit_z <dbl>, CCN_sig_z <dbl>, rsp_sig_z <dbl>, DAN_sig_z <dbl>,
## # angular_lh_sig_z <dbl>, CCN_lh_sig_z <dbl>, DAN_lh_sig_z <dbl>,
## # hipp_sig_z <dbl>, hipp_tail_sig_z <dbl>, hipp_quintile <int>,
## # rsp_quintile <int>, CCN_quintile <int>, DAN_quintile <int>,
## # angular_lh_sig_quintile <int>, CCN_lh_quintile <int>,
## # DAN_lh_quintile <int>, hipp_tail_quintile <int>, vtc_quintile <int>,
## # hipp_logit_quintile <int>, ang_quintile <int>, rGenHipp <dbl>,
## # ERActUnsigned <dbl>, ERActUnsigned_infpar <dbl>, RT_z <dbl>deq.3b<-lmer(rGenHipp ~ group*(RT_z) + reps + shockCond +
(-1 + RT_z|subid)+
(-1 + reps|subid)+
(1 |subid),
data=dCleanStandardized_HC)
# uncorrelate random slopes and intercepts
summary(deq.3b)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## rGenHipp ~ group * (RT_z) + reps + shockCond + (-1 + RT_z | subid) +
## (-1 + reps | subid) + (1 | subid)
## Data: dCleanStandardized_HC
##
## REML criterion at convergence: 7966.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5190 -0.6156 0.0194 0.6350 3.7852
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid RT_z 0.004274 0.06537
## subid.1 reps2 0.004720 0.06870
## reps4 0.002204 0.04694 -1.00
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.993363 0.99668
## Number of obs: 2797, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.488e-03 2.016e-02 1.049e+03 -0.223 0.824
## group1 5.181e-04 1.969e-02 2.753e+03 0.026 0.979
## RT_z -2.581e-02 2.269e-02 3.640e+01 -1.137 0.263
## reps1 2.273e-02 2.183e-02 3.310e+01 1.041 0.305
## shockCond1 9.091e-04 1.890e-02 2.781e+03 0.048 0.962
## group1:RT_z 7.960e-03 2.258e-02 3.570e+01 0.353 0.726
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1
## group1 -0.281
## RT_z -0.020 0.002
## reps1 -0.216 0.018 0.099
## shockCond1 -0.024 0.020 -0.014 -0.012
## group1:RT_z 0.001 0.001 -0.230 -0.003 0.016# no interaction by rep * group?
summary(lmer(rGenHipp ~ group*(RT_z) * reps + shockCond +
(-1 + RT_z|subid)+
(-1 + reps|subid)+
(1 |subid),
data=dCleanStandardized_HC))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## rGenHipp ~ group * (RT_z) * reps + shockCond + (-1 + RT_z | subid) +
## (-1 + reps | subid) + (1 | subid)
## Data: dCleanStandardized_HC
##
## REML criterion at convergence: 7981.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5271 -0.6072 0.0193 0.6348 3.8193
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid RT_z 0.004239 0.06511
## subid.1 reps2 0.004548 0.06744
## reps4 0.002117 0.04601 -1.00
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.993552 0.99677
## Number of obs: 2797, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.596e-03 2.040e-02 1.081e+03 -0.274 0.784
## group1 -4.152e-03 2.040e-02 1.080e+03 -0.204 0.839
## RT_z -2.361e-02 2.284e-02 3.690e+01 -1.034 0.308
## reps1 1.672e-02 2.245e-02 3.930e+01 0.745 0.461
## shockCond1 1.522e-04 1.891e-02 2.779e+03 0.008 0.994
## group1:RT_z 9.480e-03 2.284e-02 3.690e+01 0.415 0.680
## group1:reps1 2.784e-02 2.246e-02 3.920e+01 1.240 0.223
## RT_z:reps1 -2.193e-02 2.013e-02 2.612e+03 -1.090 0.276
## group1:RT_z:reps1 9.229e-03 2.013e-02 2.611e+03 0.458 0.647
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1 gr1:RT_ grp1:1 RT_z:1
## group1 -0.288
## RT_z -0.038 0.013
## reps1 -0.229 0.074 0.107
## shockCond1 -0.024 0.022 -0.015 -0.007
## group1:RT_z 0.013 -0.038 -0.234 -0.030 0.015
## group1:rps1 0.074 -0.229 -0.030 -0.246 -0.021 0.106
## RT_z:reps1 0.135 -0.031 -0.119 -0.036 0.009 0.040 0.013
## grp1:RT_z:1 -0.031 0.135 0.040 0.014 -0.018 -0.120 -0.036 -0.285Relatively higher hippocampal signal within HC recollection decisions does not track with faster/slower RTs. However, stronger encoding strength does predict faster RTs.
Plot
# look at mean trial count per bin
trial_counts = data.frame(with(dCleanStandardized_HC,
table(subid, reps, hipp_quintile)))
trial_counts %>% group_by(reps, hipp_quintile) %>% summarise(mean(Freq), n())## Source: local data frame [10 x 4]
## Groups: reps [?]
##
## # A tibble: 10 x 4
## reps hipp_quintile `mean(Freq)` `n()`
## <fctr> <fctr> <dbl> <int>
## 1 2 1 5.809524 42
## 2 2 2 5.500000 42
## 3 2 3 5.166667 42
## 4 2 4 5.452381 42
## 5 2 5 5.023810 42
## 6 4 1 8.000000 42
## 7 4 2 7.761905 42
## 8 4 3 8.142857 42
## 9 4 4 7.809524 42
## 10 4 5 7.928571 42## collapse
dm_avg = dm %>% filter(pcorr == 1) %>%
group_by(subid) %>%
mutate(hipp_quintile = ntile(hipp_sig, 5)) %>%
group_by(subid, group, hipp_quintile, reps) %>%
summarise(vtc_logit = mean_(respRT), count = n()) %>%
group_by(subid, group, hipp_quintile, reps) %>%
summarise(vtc_logit = mean_(vtc_logit), count = n())%>%
ungroup() %>%
mutate(reps = revalue(reps, replace = c('2' = 'weak',
'4' = 'strong')))
dfwc = summarySEwithin(dm_avg, measurevar="vtc_logit",
withinvars=c("hipp_quintile", "reps"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95)## Automatically converting the following non-factors to factors: hipp_quintiledfwc## group hipp_quintile reps N vtc_logit vtc_logit_norm sd
## 1 control 1 strong 22 2.237258 2.278182 0.1958658
## 2 control 1 weak 21 2.289339 2.338050 0.3229896
## 3 control 2 strong 22 2.102688 2.143613 0.2904568
## 4 control 2 weak 21 2.280425 2.329136 0.2782567
## 5 control 3 strong 22 2.217701 2.258626 0.1937109
## 6 control 3 weak 20 2.408259 2.480284 0.4150282
## 7 control 4 strong 22 2.223024 2.263949 0.2712205
## 8 control 4 weak 20 2.320787 2.392812 0.1631329
## 9 control 5 strong 21 2.171671 2.220383 0.1934599
## 10 control 5 weak 22 2.276409 2.317334 0.2412865
## 11 stress 1 strong 20 2.318344 2.256436 0.3073095
## 12 stress 1 weak 19 2.457823 2.396703 0.2060375
## 13 stress 2 strong 20 2.375300 2.313392 0.2314083
## 14 stress 2 weak 19 2.418364 2.392396 0.3736609
## 15 stress 3 strong 20 2.218369 2.156461 0.3834380
## 16 stress 3 weak 19 2.339948 2.313980 0.3275529
## 17 stress 4 strong 20 2.249552 2.187643 0.3613652
## 18 stress 4 weak 18 2.441740 2.364504 0.2830902
## 19 stress 5 strong 19 2.317853 2.240635 0.3220752
## 20 stress 5 weak 19 2.420286 2.394317 0.4217581
## se ci
## 1 0.04175874 0.08684205
## 2 0.07048212 0.14702313
## 3 0.06192560 0.12878134
## 4 0.06072059 0.12666093
## 5 0.04129931 0.08588661
## 6 0.09280312 0.19423916
## 7 0.05782441 0.12025244
## 8 0.03647762 0.07634853
## 9 0.04221640 0.08806187
## 10 0.05144245 0.10698044
## 11 0.06871649 0.14382527
## 12 0.04726824 0.09930689
## 13 0.05174447 0.10830242
## 14 0.08572369 0.18009879
## 15 0.08573935 0.17945452
## 16 0.07514578 0.15787543
## 17 0.08080372 0.16912413
## 18 0.06672500 0.14077743
## 19 0.07388912 0.15523528
## 20 0.09675795 0.20328090p2 = ggplot(dfwc, aes(x=hipp_quintile, y=vtc_logit, group=group, color=group)) +
facet_grid(.~reps) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=vtc_logit-se,
ymax=vtc_logit+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('RT (s)')+
xlab('Hippocampal activity (quintiles)')+
scale_color_manual(values=palette, guide = guide_legend(title = NULL))
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/respRTXhipp_quintileXrep_HCrecollection_wholehipp.jpg',
dpi=150, width = 9, height=4)
p2
VTC ~ RT
# look at trial counts/subj
with(dCleanStandardized_HC, table(subid))## subid
## ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ap110 ap111 ap113
## 87 102 66 111 146 117 122 28 58 20 73 75
## ap114 ap115 ap116 ap117 ap118 ap119 ap120 ap121 ap122 ap150 ap152 ap153
## 101 77 144 84 62 112 71 33 95 66 40 31
## ap154 ap155 ap157 ap158 ap159 ap160 ap161 ap162 ap163 ap164 ap165 ap166
## 42 38 51 11 50 51 27 41 107 81 64 71
## ap167 ap169 ap170 ap171 ap172 ap173
## 38 69 74 33 20 8deq.3b<-lmer(ERActUnsigned ~ group*(RT_z) + reps + shockCond +
(-1 + RT_z|subid)+
(-1 + reps|subid)+
(1 |subid),
data=dCleanStandardized_HC)
# uncorrelate random slopes and intercepts
summary(deq.3b)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: ERActUnsigned ~ group * (RT_z) + reps + shockCond + (-1 + RT_z |
## subid) + (-1 + reps | subid) + (1 | subid)
## Data: dCleanStandardized_HC
##
## REML criterion at convergence: 7924.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4567 -0.6545 -0.0445 0.6582 3.4570
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid RT_z 0.020752 0.14405
## subid.1 reps2 0.004469 0.06685
## reps4 0.002056 0.04535 -1.00
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.970414 0.98510
## Number of obs: 2797, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.306e-03 1.993e-02 1.081e+03 -0.417 0.6769
## group1 1.805e-03 1.946e-02 2.748e+03 0.093 0.9261
## RT_z 5.684e-02 3.060e-02 3.600e+01 1.858 0.0714 .
## reps1 3.738e-02 2.157e-02 3.970e+01 1.733 0.0908 .
## shockCond1 5.776e-03 1.873e-02 2.770e+03 0.308 0.7578
## group1:RT_z 6.038e-02 3.052e-02 3.570e+01 1.979 0.0556 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 RT_z reps1 shckC1
## group1 -0.281
## RT_z -0.015 0.002
## reps1 -0.217 0.018 0.073
## shockCond1 -0.024 0.020 -0.011 -0.012
## group1:RT_z 0.001 0.000 -0.148 -0.006 0.012summary(lmer(RT_z ~ (ERActUnsigned) + reps + shockCond +
(-1 + ERActUnsigned|subid)+
(-1 + reps|subid)+
(1 |subid),
data=dCleanStandardized_HC %>% filter(group == 'control')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## RT_z ~ (ERActUnsigned) + reps + shockCond + (-1 + ERActUnsigned |
## subid) + (-1 + reps | subid) + (1 | subid)
## Data: dCleanStandardized_HC %>% filter(group == "control")
##
## REML criterion at convergence: 5040.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2576 -0.7048 -0.1208 0.5867 3.8694
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid ERActUnsigned 0.018224 0.1350
## subid.1 reps2 0.012952 0.1138
## reps4 0.007413 0.0861 -1.00
## subid.2 (Intercept) 0.000000 0.0000
## Residual 0.944022 0.9716
## Number of obs: 1795, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.167e-02 2.362e-02 3.339e+02 0.917 0.359668
## ERActUnsigned 1.146e-01 3.806e-02 1.930e+01 3.011 0.007101 **
## reps1 -1.345e-01 3.268e-02 1.940e+01 -4.117 0.000563 ***
## shockCond1 -2.386e-03 2.302e-02 1.770e+03 -0.104 0.917467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ERActU reps1
## ERActUnsgnd 0.001
## reps1 -0.229 -0.006
## shockCond1 0.000 -0.015 -0.024summary(lmer(RT_z ~ (ERActUnsigned) + reps + shockCond +
(-1 + ERActUnsigned|subid)+
(-1 + reps|subid)+
(1 |subid),
data=dCleanStandardized_HC %>% filter(group == 'stress')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## RT_z ~ (ERActUnsigned) + reps + shockCond + (-1 + ERActUnsigned |
## subid) + (-1 + reps | subid) + (1 | subid)
## Data: dCleanStandardized_HC %>% filter(group == "stress")
##
## REML criterion at convergence: 2834.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4034 -0.6923 -0.1090 0.5793 4.2441
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid ERActUnsigned 0.024211 0.15560
## subid.1 reps2 0.006391 0.07994
## reps4 0.002720 0.05215 -1.00
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.957562 0.97855
## Number of obs: 1002, groups: subid, 20
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.026631 0.031998 325.200000 0.832 0.40586
## ERActUnsigned -0.001113 0.048060 14.600000 -0.023 0.98184
## reps1 -0.129381 0.035744 11.200000 -3.620 0.00391 **
## shockCond1 0.028627 0.031132 986.800000 0.920 0.35804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ERActU reps1
## ERActUnsgnd 0.009
## reps1 -0.247 -0.035
## shockCond1 -0.053 0.017 0.018# interaction by rep * group?
fit1 = lmer(RT_z ~ group*(ERActUnsigned) * reps + shockCond +
(-1 + ERActUnsigned|subid)+
(-1 + reps|subid)+
(1 |subid),
data=dCleanStandardized_HC)
anova(deq.3b, fit1) ## refitting model(s) with ML (instead of REML)## Data: dCleanStandardized_HC
## Models:
## object: ERActUnsigned ~ group * (RT_z) + reps + shockCond + (-1 + RT_z |
## object: subid) + (-1 + reps | subid) + (1 | subid)
## ..1: RT_z ~ group * (ERActUnsigned) * reps + shockCond + (-1 + ERActUnsigned |
## ..1: subid) + (-1 + reps | subid) + (1 | subid)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## object 12 7914.2 7985.4 -3945.1 7890.2
## ..1 15 7864.5 7953.5 -3917.2 7834.5 55.714 3 4.834e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(fit1)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## RT_z ~ group * (ERActUnsigned) * reps + shockCond + (-1 + ERActUnsigned |
## subid) + (-1 + reps | subid) + (1 | subid)
## Data: dCleanStandardized_HC
##
## REML criterion at convergence: 7886.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3822 -0.6994 -0.1166 0.5873 4.2232
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid ERActUnsigned 0.019811 0.14075
## subid.1 reps2 0.011384 0.10670
## reps4 0.005891 0.07676 -1.00
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.948960 0.97415
## Number of obs: 2797, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.385e-02 1.989e-02 7.036e+02 1.199
## group1 -2.112e-03 1.989e-02 7.022e+02 -0.106
## ERActUnsigned 5.004e-02 3.056e-02 3.830e+01 1.637
## reps1 -1.329e-01 2.495e-02 3.850e+01 -5.325
## shockCond1 9.005e-03 1.851e-02 2.756e+03 0.486
## group1:ERActUnsigned 6.298e-02 3.056e-02 3.830e+01 2.060
## group1:reps1 -3.034e-03 2.496e-02 3.850e+01 -0.122
## ERActUnsigned:reps1 2.110e-02 2.030e-02 2.756e+03 1.040
## group1:ERActUnsigned:reps1 -1.598e-02 2.030e-02 2.756e+03 -0.787
## Pr(>|t|)
## (Intercept) 0.2309
## group1 0.9155
## ERActUnsigned 0.1098
## reps1 4.63e-06 ***
## shockCond1 0.6267
## group1:ERActUnsigned 0.0462 *
## group1:reps1 0.9039
## ERActUnsigned:reps1 0.2986
## group1:ERActUnsigned:reps1 0.4313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 ERActU reps1 shckC1 gr1:ERAU grp1:1 ERAU:1
## group1 -0.289
## ERActUnsgnd 0.015 -0.013
## reps1 -0.245 0.067 -0.027
## shockCond1 -0.026 0.025 0.000 -0.003
## grp1:ERActU -0.012 0.015 -0.167 0.022 -0.015
## group1:rps1 0.068 -0.245 0.022 -0.204 -0.021 -0.027
## ERActUnsg:1 -0.037 0.029 -0.191 0.019 0.001 0.089 -0.018
## grp1:ERAU:1 0.028 -0.037 0.090 -0.018 0.002 -0.191 0.019 -0.311Plot
# look at mean trial count per bin
trial_counts = data.frame(with(dCleanStandardized_HC,
table(subid, reps, vtc_quintile)))
trial_counts %>% group_by(reps, vtc_quintile) %>% summarise(mean(Freq), n())## Source: local data frame [10 x 4]
## Groups: reps [?]
##
## # A tibble: 10 x 4
## reps vtc_quintile `mean(Freq)` `n()`
## <fctr> <fctr> <dbl> <int>
## 1 2 1 5.619048 42
## 2 2 2 5.380952 42
## 3 2 3 5.738095 42
## 4 2 4 5.333333 42
## 5 2 5 4.880952 42
## 6 4 1 8.190476 42
## 7 4 2 7.880952 42
## 8 4 3 7.571429 42
## 9 4 4 7.928571 42
## 10 4 5 8.071429 42## collapse
dm_avg = dm %>% filter(pcorr == 1) %>%
group_by(subid) %>%
mutate(hipp_quintile = ntile(vtc_logit, 5)) %>%
group_by(subid, group, hipp_quintile, reps) %>%
summarise(vtc_logit = mean_(respRT), count = n()) %>%
group_by(subid, group, hipp_quintile, reps) %>%
summarise(vtc_logit = mean_(vtc_logit), count = n())%>%
ungroup() %>%
mutate(reps = revalue(reps, replace = c('2' = 'weak',
'4' = 'strong')))
dfwc = summarySEwithin(dm_avg, measurevar="vtc_logit",
withinvars=c("hipp_quintile", "reps"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95)## Automatically converting the following non-factors to factors: hipp_quintiledfwc## group hipp_quintile reps N vtc_logit vtc_logit_norm sd
## 1 control 1 strong 21 2.126206 2.158555 0.2411987
## 2 control 1 weak 21 2.165570 2.236173 0.2564896
## 3 control 2 strong 22 2.117715 2.164387 0.1326260
## 4 control 2 weak 20 2.320329 2.398857 0.2785264
## 5 control 3 strong 22 2.175439 2.222111 0.1406605
## 6 control 3 weak 20 2.255315 2.293676 0.2611323
## 7 control 4 strong 22 2.248660 2.295332 0.1970605
## 8 control 4 weak 20 2.230069 2.308596 0.2438504
## 9 control 5 strong 22 2.268951 2.315623 0.2419571
## 10 control 5 weak 22 2.417237 2.463908 0.2859700
## 11 stress 1 strong 20 2.271066 2.197821 0.2860564
## 12 stress 1 weak 20 2.475159 2.401914 0.3026667
## 13 stress 2 strong 20 2.302424 2.229179 0.3304546
## 14 stress 2 weak 19 2.392177 2.356774 0.3661500
## 15 stress 3 strong 19 2.279786 2.244384 0.2105916
## 16 stress 3 weak 20 2.444459 2.371214 0.3227497
## 17 stress 4 strong 20 2.245060 2.171815 0.3810657
## 18 stress 4 weak 18 2.373941 2.341402 0.2911356
## 19 stress 5 strong 20 2.261222 2.187977 0.3124220
## 20 stress 5 weak 17 2.397721 2.369261 0.5264578
## se ci
## 1 0.05263388 0.10979235
## 2 0.05597061 0.11675265
## 3 0.02827595 0.05880306
## 4 0.06228040 0.13035438
## 5 0.02998892 0.06236536
## 6 0.05839095 0.12221367
## 7 0.04201344 0.08737173
## 8 0.05452660 0.11412549
## 9 0.05158543 0.10727776
## 10 0.06096900 0.12679198
## 11 0.06396416 0.13387852
## 12 0.06767834 0.14165239
## 13 0.07389189 0.15465749
## 14 0.08400057 0.17647864
## 15 0.04831304 0.10150192
## 16 0.07216902 0.15105149
## 17 0.08520887 0.17834422
## 18 0.06862132 0.14477833
## 19 0.06985968 0.14621799
## 20 0.12768478 0.27067963p2 = ggplot(dfwc, aes(x=hipp_quintile, y=vtc_logit, group=group, color=group)) +
facet_grid(.~reps) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=vtc_logit-se,
ymax=vtc_logit+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('RT (s)')+
xlab('Reinstatement strength (logits)')+
scale_color_manual(values=palette, guide = guide_legend(title = NULL))
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/respRTXvtc_quintileXrep_HCrecollection.jpg',
dpi=150, width = 9, height=4)
p2
2b (iv): What about across all accurate (high AND low confidence) associative judgments (i.e., like Alan’s study)
# standardize continuous vars
dCleanStandardized_H = dm %>% filter(acc == 'H')
dCleanStandardized_H = dCleanStandardized_H %>%
group_by(subid) %>%
mutate(ang_logit_z = zscore(ang_logit),
vtc_logit_z = zscore(vtc_logit),
hipp_logit_z = zscore(hipp_logit),
CCN_sig_z = zscore(CCN_sig),
DAN_sig_z = zscore(DAN_sig),
hipp_sig_z = zscore(hipp_sig),
hipp_tail_sig_z = zscore(hipp_tail_sig),
hipp_quintile = ntile(hipp_sig, 5),
CCN_quintile = ntile(CCN_sig, 5),
DAN_quintile = ntile(DAN_sig, 5),
hipp_tail_quintile = ntile(hipp_tail_sig, 5),
vtc_quintile = ntile(vtc_logit, 5),
hipp_logit_quintile = ntile(hipp_logit, 5),
ang_quintile = ntile(ang_logit, 5),
rt_z = zscore(respRT))
dCleanStandardized_H$rGenHipp<-scale(dCleanStandardized_H$hipp_sig_z)
dCleanStandardized_H$ERActUnsigned<-scale(dCleanStandardized_H$vtc_logit_z)
dCleanStandardized_H$ERActUnsigned_infpar<-scale(dCleanStandardized_H$ang_logit_z)
dCleanStandardized_H$RT_z<-scale(dCleanStandardized_H$rt_z)
# look at trial counts/subj
with(dCleanStandardized_H, table(subid))## subid
## ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ap110 ap111 ap113
## 98 116 91 124 146 129 133 70 80 75 103 108
## ap114 ap115 ap116 ap117 ap118 ap119 ap120 ap121 ap122 ap150 ap152 ap153
## 128 105 146 97 92 129 102 90 125 109 75 80
## ap154 ap155 ap157 ap158 ap159 ap160 ap161 ap162 ap163 ap164 ap165 ap166
## 74 78 92 74 73 52 73 83 121 121 94 99
## ap167 ap169 ap170 ap171 ap172 ap173
## 58 69 104 70 72 34deq.4b<-lmer(RT_z ~ group*(rGenHipp) + reps + shockCond +
(-1 + rGenHipp|subid)+
(-1 + reps|subid)+
(1 |subid),
data=dCleanStandardized_H)
# uncorrelate random slopes and intercepts
summary(deq.4b)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: RT_z ~ group * (rGenHipp) + reps + shockCond + (-1 + rGenHipp |
## subid) + (-1 + reps | subid) + (1 | subid)
## Data: dCleanStandardized_H
##
## REML criterion at convergence: 11278.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5469 -0.7199 -0.1082 0.5965 4.0041
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid rGenHipp 0.006601 0.08125
## subid.1 reps2 0.012356 0.11116
## reps4 0.008228 0.09071 -1.00
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.967885 0.98381
## Number of obs: 3992, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.065e-03 1.602e-02 1.291e+03 0.566 0.5716
## group1 -8.642e-04 1.586e-02 3.919e+03 -0.055 0.9565
## rGenHipp -5.083e-02 2.043e-02 4.100e+01 -2.489 0.0169 *
## reps1 -1.044e-01 2.234e-02 3.800e+01 -4.674 3.72e-05 ***
## shockCond1 2.376e-02 1.562e-02 3.946e+03 1.521 0.1285
## group1:rGenHipp -4.585e-03 2.042e-02 4.100e+01 -0.225 0.8235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp reps1 shckC1
## group1 -0.182
## rGenHipp 0.003 0.000
## reps1 -0.140 0.003 -0.024
## shockCond1 -0.035 0.024 0.007 -0.011
## grp1:rGnHpp 0.002 0.000 -0.134 -0.014 -0.006# interaction by rep * group?
summary(lmer(RT_z ~ group*(rGenHipp) * reps + shockCond +
(-1 + rGenHipp|subid)+
(-1 + reps|subid)+
(1 |subid),
data=dCleanStandardized_H))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: RT_z ~ group * (rGenHipp) * reps + shockCond + (-1 + rGenHipp |
## subid) + (-1 + reps | subid) + (1 | subid)
## Data: dCleanStandardized_H
##
## REML criterion at convergence: 11293.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5692 -0.7236 -0.0997 0.5913 4.0329
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid rGenHipp 0.006649 0.08154
## subid.1 reps2 0.010691 0.10340
## reps4 0.007181 0.08474 -1.00
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.968338 0.98404
## Number of obs: 3992, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.873e-03 1.603e-02 1.462e+03 0.553 0.5800
## group1 3.654e-03 1.603e-02 1.459e+03 0.228 0.8197
## rGenHipp -4.975e-02 2.053e-02 4.200e+01 -2.423 0.0198
## reps1 -9.999e-02 2.177e-02 3.700e+01 -4.592 4.85e-05
## shockCond1 2.410e-02 1.563e-02 3.944e+03 1.542 0.1231
## group1:rGenHipp -3.703e-03 2.053e-02 4.200e+01 -0.180 0.8577
## group1:reps1 -4.155e-02 2.177e-02 3.700e+01 -1.908 0.0641
## rGenHipp:reps1 -6.611e-03 1.604e-02 3.925e+03 -0.412 0.6803
## group1:rGenHipp:reps1 -1.296e-03 1.604e-02 3.925e+03 -0.081 0.9356
##
## (Intercept)
## group1
## rGenHipp *
## reps1 ***
## shockCond1
## group1:rGenHipp
## group1:reps1 .
## rGenHipp:reps1
## group1:rGenHipp:reps1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 rGnHpp reps1 shckC1 gr1:GH grp1:1 rGnH:1
## group1 -0.180
## rGenHipp 0.006 0.003
## reps1 -0.136 0.020 -0.024
## shockCond1 -0.034 0.026 0.008 -0.011
## grp1:rGnHpp 0.003 0.006 -0.137 -0.012 -0.005
## group1:rps1 0.020 -0.136 -0.012 -0.124 -0.010 -0.024
## rGnHpp:rps1 -0.040 -0.021 -0.085 0.006 -0.007 0.033 0.001
## grp1:rGnH:1 -0.021 -0.040 0.033 0.001 -0.008 -0.085 0.006 -0.189# interaction:
summary(lmer(RT_z ~ (rGenHipp) + reps + shockCond +
(-1 + rGenHipp|subid)+
(-1 + reps|subid)+
(1 |subid),
data=dCleanStandardized_H %>% filter(group == 'control')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: RT_z ~ (rGenHipp) + reps + shockCond + (-1 + rGenHipp | subid) +
## (-1 + reps | subid) + (1 | subid)
## Data: dCleanStandardized_H %>% filter(group == "control")
##
## REML criterion at convergence: 6647.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5123 -0.7244 -0.0984 0.5751 3.9702
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid rGenHipp 0.008808 0.09385
## subid.1 reps2 0.012783 0.11306
## reps4 0.008880 0.09424 -1.00
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.957174 0.97835
## Number of obs: 2361, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.01192 0.02035 705.60000 0.586 0.558143
## rGenHipp -0.05348 0.02863 19.70000 -1.868 0.076731 .
## reps1 -0.14010 0.03021 21.30000 -4.637 0.000137 ***
## shockCond1 0.01150 0.02020 2336.70000 0.569 0.569140
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rGnHpp reps1
## rGenHipp 0.005
## reps1 -0.138 -0.036
## shockCond1 -0.010 0.001 -0.020summary(lmer(RT_z ~ (rGenHipp) + reps + shockCond +
(-1 + rGenHipp|subid)+
(-1 + reps|subid)+
(1 |subid),
data=dCleanStandardized_H %>% filter(group == 'stress')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: RT_z ~ (rGenHipp) + reps + shockCond + (-1 + rGenHipp | subid) +
## (-1 + reps | subid) + (1 | subid)
## Data: dCleanStandardized_H %>% filter(group == "stress")
##
## REML criterion at convergence: 4632.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5702 -0.7204 -0.1071 0.6211 3.9864
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid rGenHipp 0.003632 0.06027
## subid.1 reps2 0.005857 0.07653
## reps4 0.003722 0.06101 -1.00
## subid.2 (Intercept) 0.000000 0.00000
## Residual 0.984554 0.99225
## Number of obs: 1631, groups: subid, 20
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.031e-03 2.482e-02 8.169e+02 0.162 0.8710
## rGenHipp -4.738e-02 2.829e-02 2.070e+01 -1.675 0.1090
## reps1 -6.034e-02 2.936e-02 1.160e+01 -2.055 0.0630 .
## shockCond1 4.171e-02 2.465e-02 1.606e+03 1.692 0.0908 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rGnHpp reps1
## rGenHipp 0.001
## reps1 -0.127 -0.012
## shockCond1 -0.061 0.014 -0.001Plot
# trial counts are pretty good:
dm %>% filter(acc == 'H') %>%
group_by(subid) %>%
summarise(n())## # A tibble: 42 x 2
## subid `n()`
## <fctr> <int>
## 1 ap100 98
## 2 ap101 116
## 3 ap102 91
## 4 ap103 124
## 5 ap104 146
## 6 ap105 129
## 7 ap107 133
## 8 ap108 70
## 9 ap109 80
## 10 ap110 75
## # ... with 32 more rowsdm_avg = dm %>% filter(acc == 'H') %>%
group_by(subid) %>%
mutate(hipp_quintile = ntile(hipp_sig, 5)) %>%
group_by(subid, group, hipp_quintile, reps) %>%
summarise(vtc_logit = mean_(respRT), count = n()) %>%
group_by(subid, group, hipp_quintile, reps) %>%
summarise(vtc_logit = mean_(vtc_logit), count = n())%>%
ungroup() %>%
mutate(reps = revalue(reps, replace = c('2' = 'weak',
'4' = 'strong')))
dfwc = summarySEwithin(dm_avg, measurevar="vtc_logit",
withinvars=c("hipp_quintile", "reps"),
betweenvars = c('group'),
idvar="subid", na.rm=TRUE, conf.interval=.95)## Automatically converting the following non-factors to factors: hipp_quintiledfwc## group hipp_quintile reps N vtc_logit vtc_logit_norm sd
## 1 control 1 strong 22 2.246905 2.281643 0.1730714
## 2 control 1 weak 22 2.426256 2.460994 0.2527171
## 3 control 2 strong 22 2.206985 2.241723 0.1890151
## 4 control 2 weak 22 2.339579 2.374317 0.1671371
## 5 control 3 strong 22 2.220043 2.254781 0.1350646
## 6 control 3 weak 22 2.384549 2.419287 0.2274133
## 7 control 4 strong 22 2.225853 2.260591 0.2118445
## 8 control 4 weak 22 2.392101 2.426839 0.1808456
## 9 control 5 strong 22 2.134784 2.169521 0.1990704
## 10 control 5 weak 22 2.268831 2.303568 0.1761413
## 11 stress 1 strong 20 2.334059 2.295848 0.2078176
## 12 stress 1 weak 20 2.393061 2.354850 0.1612669
## 13 stress 2 strong 20 2.448409 2.410197 0.3184128
## 14 stress 2 weak 20 2.453996 2.415785 0.2350245
## 15 stress 3 strong 20 2.242104 2.203893 0.1825858
## 16 stress 3 weak 20 2.323713 2.285502 0.2654621
## 17 stress 4 strong 20 2.372911 2.334700 0.2260610
## 18 stress 4 weak 20 2.286386 2.248174 0.3168303
## 19 stress 5 strong 20 2.305387 2.267176 0.2882060
## 20 stress 5 weak 20 2.415349 2.377138 0.2697392
## se ci
## 1 0.03689894 0.07673555
## 2 0.05387947 0.11204848
## 3 0.04029815 0.08380459
## 4 0.03563375 0.07410443
## 5 0.02879587 0.05988429
## 6 0.04848468 0.10082942
## 7 0.04516540 0.09392660
## 8 0.03855641 0.08018245
## 9 0.04244196 0.08826289
## 10 0.03755345 0.07809667
## 11 0.04646943 0.09726164
## 12 0.03606037 0.07547523
## 13 0.07119928 0.14902180
## 14 0.05255308 0.10999486
## 15 0.04082743 0.08545280
## 16 0.05935912 0.12424006
## 17 0.05054877 0.10579979
## 18 0.07084541 0.14828116
## 19 0.06444482 0.13488456
## 20 0.06031551 0.12624182p2 = ggplot(dfwc, aes(x=hipp_quintile, y=vtc_logit, group=group, color=group)) +
facet_grid(.~reps) +
geom_line(position=pos_dodge, size=1.5) +
geom_errorbar(width=0, aes(ymin=vtc_logit-se,
ymax=vtc_logit+se), size=1.5, position=pos_dodge) +
geom_point(size=5, position=pos_dodge) +
ylab('RT (s)')+
xlab('Hippocampal activity (quintiles)')+
scale_color_manual(values=palette, guide = guide_legend(title = NULL))
p2
ggsave(p2,
filename ='~/Experiments/AP/figs/respRTXhipp_quintileXrep_Hhighlowconf_wholehipp.jpg',
dpi=150, width = 9, height=4)
p2
3) Effects of group/encoding strength on VTC reinstatement
Across old cues: pCorr ~ VTC reinstatement
Without controlling for hipp BOLD
dCleanStandardized %>% group_by(group, subid) %>%
summarise(n()) %>%
group_by(group)%>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 22
## 2 stress 20summary(glmer(pcorr ~ group*(ERActUnsigned) + reps + shockCond +
(-1 + ERActUnsigned|subid) +
(1|subid),
data=dCleanStandardized, family = "binomial"))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## pcorr ~ group * (ERActUnsigned) + reps + shockCond + (-1 + ERActUnsigned |
## subid) + (1 | subid)
## Data: dCleanStandardized
##
## AIC BIC logLik deviance df.resid
## 7550.8 7605.1 -3767.4 7534.8 6615
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8002 -0.6986 -0.3596 0.7835 5.6495
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid ERActUnsigned 0.04786 0.2188
## subid.1 (Intercept) 0.99038 0.9952
## Number of obs: 6623, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.441580 0.156672 -2.818 0.00482 **
## group1 0.436634 0.156660 2.787 0.00532 **
## ERActUnsigned 0.326077 0.045059 7.237 4.6e-13 ***
## reps1 0.389862 0.028704 13.582 < 2e-16 ***
## shockCond1 0.020818 0.028332 0.735 0.46246
## group1:ERActUnsigned -0.006766 0.045038 -0.150 0.88059
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 ERActU reps1 shckC1
## group1 -0.049
## ERActUnsgnd -0.009 0.006
## reps1 -0.013 0.013 -0.017
## shockCond1 -0.004 0.005 0.004 0.008
## grp1:ERActU 0.006 -0.009 -0.057 0.003 -0.002HC assoc hits: VTC reinstatement ~ group
subids_lowtrials = dm %>%
filter(cond == 'sourcehit') %>%
group_by(group, subid) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), fill = list(count= 0)) %>%
filter(count < 6) %>%
pull(subid) %>% unique()
subids_lowtrials## factor(0)
## 42 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ... ap173d_avg = dm %>% filter(!subid %in% subids_lowtrials) %>%
filter(cond == 'sourcehit') %>%
group_by(group, subid) %>%
summarise(mean=mean(vtc_logit))
with(d_avg, table(group))## group
## control stress
## 22 20# diff between groups?
bartlett.test(mean ~ group, data=d_avg)##
## Bartlett test of homogeneity of variances
##
## data: mean by group
## Bartlett's K-squared = 1.3043, df = 1, p-value = 0.2534t.test(mean ~ group, data=d_avg, var.equal=TRUE)##
## Two Sample t-test
##
## data: mean by group
## t = 1.1333, df = 40, p-value = 0.2638
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1803197 0.6406913
## sample estimates:
## mean in group control mean in group stress
## 0.9638410 0.7336551d_avg %>% group_by(group) %>% summarise(mean(mean), n())## # A tibble: 2 x 3
## group `mean(mean)` `n()`
## <fctr> <dbl> <int>
## 1 control 0.9638410 22
## 2 stress 0.7336551 20t.test(d_avg %>% filter(group == 'control') %>% pull(mean), mu = 0)##
## One Sample t-test
##
## data: d_avg %>% filter(group == "control") %>% pull(mean)
## t = 7.9087, df = 21, p-value = 9.893e-08
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.710398 1.217284
## sample estimates:
## mean of x
## 0.963841t.test(d_avg %>% filter(group == 'stress') %>% pull(mean), mu = 0)##
## One Sample t-test
##
## data: d_avg %>% filter(group == "stress") %>% pull(mean)
## t = 4.4292, df = 19, p-value = 0.0002879
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.3869672 1.0803431
## sample estimates:
## mean of x
## 0.7336551Does this hold in VTC excluding parahipp?
d_avg = dm %>% filter(!subid %in% subids_lowtrials) %>%
filter(cond == 'sourcehit') %>%
group_by(group, subid) %>%
summarise(mean=mean(vtc_nophc_logit))
with(d_avg, table(group))## group
## control stress
## 22 20# diff between groups?
bartlett.test(mean ~ group, data=d_avg)##
## Bartlett test of homogeneity of variances
##
## data: mean by group
## Bartlett's K-squared = 1.2018, df = 1, p-value = 0.273t.test(mean ~ group, data=d_avg, var.equal=TRUE)##
## Two Sample t-test
##
## data: mean by group
## t = 1.2401, df = 40, p-value = 0.2222
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1522148 0.6355816
## sample estimates:
## mean in group control mean in group stress
## 0.7124270 0.4707436d_avg %>% group_by(group) %>% summarise(mean(mean), n())## # A tibble: 2 x 3
## group `mean(mean)` `n()`
## <fctr> <dbl> <int>
## 1 control 0.7124270 22
## 2 stress 0.4707436 20t.test(d_avg %>% filter(group == 'control') %>% pull(mean), mu = 0)##
## One Sample t-test
##
## data: d_avg %>% filter(group == "control") %>% pull(mean)
## t = 6.0541, df = 21, p-value = 5.23e-06
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.4677051 0.9571489
## sample estimates:
## mean of x
## 0.712427t.test(d_avg %>% filter(group == 'stress') %>% pull(mean), mu = 0)##
## One Sample t-test
##
## data: d_avg %>% filter(group == "stress") %>% pull(mean)
## t = 2.9742, df = 19, p-value = 0.007794
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.1394678 0.8020194
## sample estimates:
## mean of x
## 0.4707436Distribution of HC assoc hit logits by subj
trial_type = 'sourcehit'
subids_lowtrials = dm %>%
filter(cond == trial_type) %>%
group_by(group, subid) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), fill = list(count= 0)) %>%
filter(count < 6) %>%
pull(subid) %>% unique()
subids_lowtrials## factor(0)
## 42 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ... ap173d_avg = dm %>% filter(!subid %in% subids_lowtrials) %>%
filter(cond == trial_type) %>%
mutate(subid = factor(subid))
with(d_avg, table(subid))## subid
## ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ap110 ap111 ap113
## 87 102 66 111 146 117 122 28 58 20 73 75
## ap114 ap115 ap116 ap117 ap118 ap119 ap120 ap121 ap122 ap150 ap152 ap153
## 101 77 144 84 62 112 71 33 95 66 40 31
## ap154 ap155 ap157 ap158 ap159 ap160 ap161 ap162 ap163 ap164 ap165 ap166
## 42 38 51 11 50 51 27 41 107 81 64 71
## ap167 ap169 ap170 ap171 ap172 ap173
## 38 69 74 33 20 8head(d_avg)## # A tibble: 6 x 83
## X.x run trial onset duration shockCond shockTrial target
## <int> <dbl> <int> <dbl> <dbl> <fctr> <int> <fctr>
## 1 2 1 3 19.8642 9.4224 safe 0 VIOLIN
## 2 3 1 4 29.3132 11.3389 safe 0 MEDAL
## 3 4 1 5 40.6786 9.7644 safe 0 MANURE
## 4 5 1 6 50.4701 9.1180 safe 0 SARDINE
## 5 14 1 15 137.3692 10.3760 safe 0 SEAL
## 6 15 1 16 147.7725 11.9447 safe 0 SCROLL
## # ... with 75 more variables: associate <fctr>, resp <fctr>, acc <fctr>,
## # accSpec <fctr>, respRT <dbl>, subid <fctr>, remove <lgl>,
## # anxious <int>, happy <int>, safe <int>, stressed <int>,
## # life_stress <int>, sleep <dbl>, cond_orig <fctr>,
## # shock_and_post <int>, conf <fctr>, onset_adj <dbl>,
## # mem_conditions <fctr>, obj_conditions <fctr>, onset_code <fctr>,
## # rt_z <dbl>, mem_conditions_byrep <fctr>, onset_code_byrep <fctr>,
## # img_type <fctr>, X.y <int>, index <int>, group <fctr>,
## # condition <fctr>, category <fctr>, X0 <dbl>, X2 <dbl>, X4 <dbl>,
## # X6 <dbl>, X8 <dbl>, X10 <dbl>, X12 <dbl>, avg_logit <dbl>,
## # cond <fctr>, reps <fctr>, pcorr <fctr>, vtc_logit <dbl>,
## # imgType <fctr>, hipp_sig <dbl>, hipp_tail_sig <dbl>, DAN_sig <dbl>,
## # CCN_sig <dbl>, rsp_sig <dbl>, angular_lh_sig <dbl>, DAN_lh_sig <dbl>,
## # CCN_lh_sig <dbl>, ang_logit <dbl>, hipp_logit <dbl>,
## # vtc_nophc_logit <dbl>, ang_logit_z <dbl>, vtc_logit_z <dbl>,
## # hipp_logit_z <dbl>, CCN_sig_z <dbl>, rsp_sig_z <dbl>, DAN_sig_z <dbl>,
## # angular_lh_sig_z <dbl>, CCN_lh_sig_z <dbl>, DAN_lh_sig_z <dbl>,
## # hipp_sig_z <dbl>, hipp_tail_sig_z <dbl>, hipp_quintile <int>,
## # rsp_quintile <int>, CCN_quintile <int>, DAN_quintile <int>,
## # angular_lh_sig_quintile <int>, CCN_lh_quintile <int>,
## # DAN_lh_quintile <int>, hipp_tail_quintile <int>, vtc_quintile <int>,
## # hipp_logit_quintile <int>, ang_quintile <int>d_avg %>% group_by(subid, group, cond) %>% summarise(mean = mean(vtc_logit),
stdev = sd(vtc_logit)) %>%
group_by(group, cond) %>% summarise(mean(mean), std.error(mean), n(),
mean(stdev))## Source: local data frame [2 x 6]
## Groups: group [?]
##
## # A tibble: 2 x 6
## group cond `mean(mean)` `std.error(mean)` `n()` `mean(stdev)`
## <fctr> <fctr> <dbl> <dbl> <int> <dbl>
## 1 control sourcehit 0.9638410 0.1218702 22 2.718126
## 2 stress sourcehit 0.7336551 0.1656397 20 2.682818ggplot(d_avg, aes(x=vtc_logit, group=subid, color=subid)) +
geom_density() +
facet_grid(group~.) +
guides(colour=FALSE)
ggsave('/Volumes/group/awagner/sgagnon/AP/results/vtclogit_dist_sourcehit.png')## Saving 7 x 5 in imagetrial_type = 'itemhit_lo'
subids_lowtrials = dm %>%
filter(cond == trial_type) %>%
group_by(group, subid) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), fill = list(count= 0)) %>%
filter(count < 6) %>%
pull(subid) %>% unique()
subids_lowtrials## [1] ap116 ap160 ap169
## 42 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ... ap173d_avg = dm %>% filter(!subid %in% subids_lowtrials) %>%
filter(cond == trial_type) %>%
mutate(subid = factor(subid))
with(d_avg, table(subid))## subid
## ap100 ap101 ap102 ap103 ap105 ap107 ap108 ap109 ap110 ap111 ap113 ap114
## 25 21 61 22 17 17 68 45 106 68 52 50
## ap115 ap117 ap118 ap119 ap120 ap121 ap122 ap150 ap152 ap153 ap154 ap155
## 43 25 56 26 57 108 47 72 70 86 58 77
## ap157 ap158 ap159 ap161 ap162 ap163 ap164 ap165 ap166 ap167 ap170 ap171
## 78 115 51 87 78 29 66 55 51 36 55 94
## ap172 ap173
## 124 54head(d_avg)## # A tibble: 6 x 83
## X.x run trial onset duration shockCond shockTrial target
## <int> <dbl> <int> <dbl> <dbl> <fctr> <int> <fctr>
## 1 27 1 28 272.7073 10.9982 safe 0 POWDER
## 2 28 1 29 283.7324 12.3318 safe 0 PEDAL
## 3 50 2 9 78.7475 9.8963 threat 0 SLIPPER
## 4 58 2 17 166.1540 11.1869 threat 0 CARROT
## 5 64 2 23 226.6075 11.2677 threat 0 TURTLE
## 6 72 2 31 306.9353 9.5357 threat 0 PAN
## # ... with 75 more variables: associate <fctr>, resp <fctr>, acc <fctr>,
## # accSpec <fctr>, respRT <dbl>, subid <fctr>, remove <lgl>,
## # anxious <int>, happy <int>, safe <int>, stressed <int>,
## # life_stress <int>, sleep <dbl>, cond_orig <fctr>,
## # shock_and_post <int>, conf <fctr>, onset_adj <dbl>,
## # mem_conditions <fctr>, obj_conditions <fctr>, onset_code <fctr>,
## # rt_z <dbl>, mem_conditions_byrep <fctr>, onset_code_byrep <fctr>,
## # img_type <fctr>, X.y <int>, index <int>, group <fctr>,
## # condition <fctr>, category <fctr>, X0 <dbl>, X2 <dbl>, X4 <dbl>,
## # X6 <dbl>, X8 <dbl>, X10 <dbl>, X12 <dbl>, avg_logit <dbl>,
## # cond <fctr>, reps <fctr>, pcorr <fctr>, vtc_logit <dbl>,
## # imgType <fctr>, hipp_sig <dbl>, hipp_tail_sig <dbl>, DAN_sig <dbl>,
## # CCN_sig <dbl>, rsp_sig <dbl>, angular_lh_sig <dbl>, DAN_lh_sig <dbl>,
## # CCN_lh_sig <dbl>, ang_logit <dbl>, hipp_logit <dbl>,
## # vtc_nophc_logit <dbl>, ang_logit_z <dbl>, vtc_logit_z <dbl>,
## # hipp_logit_z <dbl>, CCN_sig_z <dbl>, rsp_sig_z <dbl>, DAN_sig_z <dbl>,
## # angular_lh_sig_z <dbl>, CCN_lh_sig_z <dbl>, DAN_lh_sig_z <dbl>,
## # hipp_sig_z <dbl>, hipp_tail_sig_z <dbl>, hipp_quintile <int>,
## # rsp_quintile <int>, CCN_quintile <int>, DAN_quintile <int>,
## # angular_lh_sig_quintile <int>, CCN_lh_quintile <int>,
## # DAN_lh_quintile <int>, hipp_tail_quintile <int>, vtc_quintile <int>,
## # hipp_logit_quintile <int>, ang_quintile <int>d_avg %>% group_by(subid, group, cond) %>% summarise(mean = mean(vtc_logit),
stdev = sd(vtc_logit)) %>%
group_by(group, cond) %>% summarise(mean(mean), std.error(mean), n(),
mean(stdev))## Source: local data frame [2 x 6]
## Groups: group [?]
##
## # A tibble: 2 x 6
## group cond `mean(mean)` `std.error(mean)` `n()` `mean(stdev)`
## <fctr> <fctr> <dbl> <dbl> <int> <dbl>
## 1 control itemhit_lo -0.08993809 0.1549688 20 2.562108
## 2 stress itemhit_lo -0.08115305 0.1128136 18 2.639425ggplot(d_avg, aes(x=vtc_logit, group=subid, color=subid)) +
geom_density() +
facet_grid(group~.) +
guides(colour=FALSE)
ggsave('/Volumes/group/awagner/sgagnon/AP/results/vtclogit_dist_itemhit.png')## Saving 7 x 5 in imagesubids_lowtrials = dm %>%
filter(cond %in% c('sourcehit', 'itemhit_lo')) %>%
mutate(cond = factor(cond)) %>%
group_by(group, subid, cond) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), cond, fill = list(count= 0)) %>%
filter(count < 6) %>%
pull(subid) %>% unique()
subids_lowtrials## [1] ap104 ap116 ap160 ap169
## 42 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ... ap173d_avg = dm %>% filter(!subid %in% subids_lowtrials) %>%
filter(cond %in% c('sourcehit', 'itemhit_lo')) %>%
mutate(subid = factor(subid),
cond = factor(cond))
with(d_avg, table(subid, cond))## cond
## subid itemhit_lo sourcehit
## ap100 25 87
## ap101 21 102
## ap102 61 66
## ap103 22 111
## ap105 17 117
## ap107 17 122
## ap108 68 28
## ap109 45 58
## ap110 106 20
## ap111 68 73
## ap113 52 75
## ap114 50 101
## ap115 43 77
## ap117 25 84
## ap118 56 62
## ap119 26 112
## ap120 57 71
## ap121 108 33
## ap122 47 95
## ap150 72 66
## ap152 70 40
## ap153 86 31
## ap154 58 42
## ap155 77 38
## ap157 78 51
## ap158 115 11
## ap159 51 50
## ap161 87 27
## ap162 78 41
## ap163 29 107
## ap164 66 81
## ap165 55 64
## ap166 51 71
## ap167 36 38
## ap170 55 74
## ap171 94 33
## ap172 124 20
## ap173 54 8head(d_avg)## # A tibble: 6 x 83
## X.x run trial onset duration shockCond shockTrial target
## <int> <dbl> <int> <dbl> <dbl> <fctr> <int> <fctr>
## 1 2 1 3 19.8642 9.4224 safe 0 VIOLIN
## 2 3 1 4 29.3132 11.3389 safe 0 MEDAL
## 3 4 1 5 40.6786 9.7644 safe 0 MANURE
## 4 5 1 6 50.4701 9.1180 safe 0 SARDINE
## 5 14 1 15 137.3692 10.3760 safe 0 SEAL
## 6 15 1 16 147.7725 11.9447 safe 0 SCROLL
## # ... with 75 more variables: associate <fctr>, resp <fctr>, acc <fctr>,
## # accSpec <fctr>, respRT <dbl>, subid <fctr>, remove <lgl>,
## # anxious <int>, happy <int>, safe <int>, stressed <int>,
## # life_stress <int>, sleep <dbl>, cond_orig <fctr>,
## # shock_and_post <int>, conf <fctr>, onset_adj <dbl>,
## # mem_conditions <fctr>, obj_conditions <fctr>, onset_code <fctr>,
## # rt_z <dbl>, mem_conditions_byrep <fctr>, onset_code_byrep <fctr>,
## # img_type <fctr>, X.y <int>, index <int>, group <fctr>,
## # condition <fctr>, category <fctr>, X0 <dbl>, X2 <dbl>, X4 <dbl>,
## # X6 <dbl>, X8 <dbl>, X10 <dbl>, X12 <dbl>, avg_logit <dbl>,
## # cond <fctr>, reps <fctr>, pcorr <fctr>, vtc_logit <dbl>,
## # imgType <fctr>, hipp_sig <dbl>, hipp_tail_sig <dbl>, DAN_sig <dbl>,
## # CCN_sig <dbl>, rsp_sig <dbl>, angular_lh_sig <dbl>, DAN_lh_sig <dbl>,
## # CCN_lh_sig <dbl>, ang_logit <dbl>, hipp_logit <dbl>,
## # vtc_nophc_logit <dbl>, ang_logit_z <dbl>, vtc_logit_z <dbl>,
## # hipp_logit_z <dbl>, CCN_sig_z <dbl>, rsp_sig_z <dbl>, DAN_sig_z <dbl>,
## # angular_lh_sig_z <dbl>, CCN_lh_sig_z <dbl>, DAN_lh_sig_z <dbl>,
## # hipp_sig_z <dbl>, hipp_tail_sig_z <dbl>, hipp_quintile <int>,
## # rsp_quintile <int>, CCN_quintile <int>, DAN_quintile <int>,
## # angular_lh_sig_quintile <int>, CCN_lh_quintile <int>,
## # DAN_lh_quintile <int>, hipp_tail_quintile <int>, vtc_quintile <int>,
## # hipp_logit_quintile <int>, ang_quintile <int>d_avg %>% group_by(subid, group, cond) %>% summarise(mean = mean(vtc_logit),
stdev = sd(vtc_logit)) %>%
group_by(group, cond) %>% summarise(mean(mean), std.error(mean), n(),
mean(stdev))## Source: local data frame [4 x 6]
## Groups: group [?]
##
## # A tibble: 4 x 6
## group cond `mean(mean)` `std.error(mean)` `n()` `mean(stdev)`
## <fctr> <fctr> <dbl> <dbl> <int> <dbl>
## 1 control itemhit_lo -0.08993809 0.1549688 20 2.562108
## 2 control sourcehit 0.97147312 0.1342191 20 2.746559
## 3 stress itemhit_lo -0.08115305 0.1128136 18 2.639425
## 4 stress sourcehit 0.78288505 0.1771069 18 2.682779t.test(mean ~ cond, var.equal=TRUE, paired=TRUE,
d_avg %>% group_by(subid, group, cond) %>% summarise(mean = mean(vtc_logit),
stdev = sd(vtc_logit)))##
## Paired t-test
##
## data: mean by cond
## t = -6.4858, df = 37, p-value = 1.385e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.270299 -0.665538
## sample estimates:
## mean of the differences
## -0.9679187contrasts(d_avg$cond) = c(1,-1); contrasts(d_avg$cond)## [,1]
## itemhit_lo 1
## sourcehit -1summary(lmer(mean ~ cond * group + (1|subid),
data=d_avg %>% group_by(subid, group, cond) %>%
summarise(mean = mean(vtc_logit),
stdev = sd(vtc_logit))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: mean ~ cond * group + (1 | subid)
## Data:
## d_avg %>% group_by(subid, group, cond) %>% summarise(mean = mean(vtc_logit),
## stdev = sd(vtc_logit))
##
## REML criterion at convergence: 157.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6428 -0.4479 -0.0079 0.5931 3.2156
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.0000 0.0000
## Residual 0.4092 0.6397
## Number of obs: 76, groups: subid, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.39582 0.07348 72.00000 5.387 8.61e-07 ***
## cond1 -0.48136 0.07348 72.00000 -6.551 7.36e-09 ***
## group1 0.04495 0.07348 72.00000 0.612 0.543
## cond1:group1 -0.04934 0.07348 72.00000 -0.672 0.504
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cond1 group1
## cond1 0.000
## group1 -0.053 0.000
## cond1:grop1 0.000 -0.053 0.000Does trial count influence reinstatement?
d_avg = dm %>%
filter(cond == 'sourcehit') %>%
filter(!subid %in% subids_lowtrials) %>%
group_by(group, subid) %>%
summarise(mean=mean(vtc_logit), n=n())
fit = lm(mean ~ n, data=d_avg)
summary(fit)##
## Call:
## lm(formula = mean ~ n, data = d_avg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.79755 -0.26772 0.09241 0.39968 1.66764
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.730130 0.247640 2.948 0.00558 **
## n 0.002420 0.003532 0.685 0.49760
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6783 on 36 degrees of freedom
## Multiple R-squared: 0.01287, Adjusted R-squared: -0.01455
## F-statistic: 0.4695 on 1 and 36 DF, p-value: 0.4976plot(fit)
with(d_avg, plot(n, mean))
boxplot(d_avg$mean)
hist(fit$residuals)
# robust regression: still n.s.
# summary(rr.huber <- rlm(mean ~ poly(n,2), data=d_avg))
#dd = data.frame(summary(rr.huber)$coefficients)
#dd$p.value = 2*pt(abs(dd$t.value), summary(rr.huber)$df[2], lower.tail=FALSE)
#ddHC assoc hits: VTC reinstatement ~ group * encoding strength
subids_lowtrials = dm %>%
filter(cond == 'sourcehit') %>%
group_by(group, subid, reps) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), reps, fill = list(count= 0)) %>%
filter(count < 6) %>% pull(subid) %>% unique()
subids_lowtrials## [1] ap110 ap158 ap173
## 42 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ... ap173dm %>% filter(!subid %in% subids_lowtrials) %>%
filter(cond == 'sourcehit') %>%
group_by(group, subid, reps) %>%
summarise(mean = mean(vtc_logit)) %>%
group_by(group, reps) %>%
summarise(mean(mean), n())## Source: local data frame [4 x 4]
## Groups: group [?]
##
## # A tibble: 4 x 4
## group reps `mean(mean)` `n()`
## <fctr> <fctr> <dbl> <int>
## 1 control 2 0.8457104 20
## 2 control 4 1.0123062 20
## 3 stress 2 0.5001697 19
## 4 stress 4 0.8331011 19contrasts(dm$group)## [,1]
## control 1
## stress -1contrasts(dm$reps)## [,1]
## 2 -1
## 4 1dm %>% filter(!subid %in% subids_lowtrials) %>%
filter(cond == 'sourcehit') %>%
group_by(group,subid) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 20
## 2 stress 19# control=20, stress=19
dat = dm %>% filter(!subid %in% subids_lowtrials) %>%
filter(cond == 'sourcehit')
dat## # A tibble: 2,758 x 83
## X.x run trial onset duration shockCond shockTrial target
## <int> <dbl> <int> <dbl> <dbl> <fctr> <int> <fctr>
## 1 2 1 3 19.8642 9.4224 safe 0 VIOLIN
## 2 3 1 4 29.3132 11.3389 safe 0 MEDAL
## 3 4 1 5 40.6786 9.7644 safe 0 MANURE
## 4 5 1 6 50.4701 9.1180 safe 0 SARDINE
## 5 14 1 15 137.3692 10.3760 safe 0 SEAL
## 6 15 1 16 147.7725 11.9447 safe 0 SCROLL
## 7 18 1 19 180.4083 9.3927 safe 0 CORAL
## 8 19 1 20 189.8279 9.5393 safe 0 ANCHOR
## 9 20 1 21 199.3947 9.7577 safe 0 TYPEWRITER
## 10 21 1 22 209.1792 11.6825 safe 0 PASSPORT
## # ... with 2,748 more rows, and 75 more variables: associate <fctr>,
## # resp <fctr>, acc <fctr>, accSpec <fctr>, respRT <dbl>, subid <fctr>,
## # remove <lgl>, anxious <int>, happy <int>, safe <int>, stressed <int>,
## # life_stress <int>, sleep <dbl>, cond_orig <fctr>,
## # shock_and_post <int>, conf <fctr>, onset_adj <dbl>,
## # mem_conditions <fctr>, obj_conditions <fctr>, onset_code <fctr>,
## # rt_z <dbl>, mem_conditions_byrep <fctr>, onset_code_byrep <fctr>,
## # img_type <fctr>, X.y <int>, index <int>, group <fctr>,
## # condition <fctr>, category <fctr>, X0 <dbl>, X2 <dbl>, X4 <dbl>,
## # X6 <dbl>, X8 <dbl>, X10 <dbl>, X12 <dbl>, avg_logit <dbl>,
## # cond <fctr>, reps <fctr>, pcorr <fctr>, vtc_logit <dbl>,
## # imgType <fctr>, hipp_sig <dbl>, hipp_tail_sig <dbl>, DAN_sig <dbl>,
## # CCN_sig <dbl>, rsp_sig <dbl>, angular_lh_sig <dbl>, DAN_lh_sig <dbl>,
## # CCN_lh_sig <dbl>, ang_logit <dbl>, hipp_logit <dbl>,
## # vtc_nophc_logit <dbl>, ang_logit_z <dbl>, vtc_logit_z <dbl>,
## # hipp_logit_z <dbl>, CCN_sig_z <dbl>, rsp_sig_z <dbl>, DAN_sig_z <dbl>,
## # angular_lh_sig_z <dbl>, CCN_lh_sig_z <dbl>, DAN_lh_sig_z <dbl>,
## # hipp_sig_z <dbl>, hipp_tail_sig_z <dbl>, hipp_quintile <int>,
## # rsp_quintile <int>, CCN_quintile <int>, DAN_quintile <int>,
## # angular_lh_sig_quintile <int>, CCN_lh_quintile <int>,
## # DAN_lh_quintile <int>, hipp_tail_quintile <int>, vtc_quintile <int>,
## # hipp_logit_quintile <int>, ang_quintile <int>summary(lmer(vtc_logit ~ group * reps +
(1 + reps|subid),
data=dat,
control=lmerControl(optimizer="Nelder_Mead",
optCtrl=list(maxfun=2e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: vtc_logit ~ group * reps + (1 + reps | subid)
## Data: dat
## Control:
## lmerControl(optimizer = "Nelder_Mead", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 13504.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4855 -0.6335 -0.0398 0.6138 4.0442
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.2905 0.5390
## reps1 0.0297 0.1723 0.08
## Residual 7.6478 2.7655
## Number of obs: 2758, groups: subid, 39
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.83234 0.10469 35.15000 7.951 2.3e-09 ***
## group1 0.10760 0.10469 35.15000 1.028 0.311
## reps1 0.08505 0.06344 44.33000 1.341 0.187
## group1:reps1 -0.03762 0.06344 44.33000 -0.593 0.556
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 reps1
## group1 -0.108
## reps1 -0.075 0.040
## group1:rps1 0.040 -0.075 -0.238HC assoc hits: VTC reinstatement ~ group * run type (safe, threat)
subids_lowtrials = dm %>%
filter(cond == 'sourcehit') %>%
group_by(group, subid, shockCond) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), shockCond, fill = list(count= 0)) %>%
filter(count < 6) %>% pull(subid) %>% unique()
subids_lowtrials## [1] ap158 ap173
## 42 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ... ap173dm %>% filter(!subid %in% subids_lowtrials) %>%
filter(cond == 'sourcehit') %>%
group_by(group, subid, shockCond) %>%
summarise(mean = mean(vtc_logit)) %>%
group_by(group, shockCond) %>%
summarise(mean(mean), n())## Source: local data frame [4 x 4]
## Groups: group [?]
##
## # A tibble: 4 x 4
## group shockCond `mean(mean)` `n()`
## <fctr> <fctr> <dbl> <int>
## 1 control safe 1.0297421 21
## 2 control threat 0.9064302 21
## 3 stress safe 0.6893210 19
## 4 stress threat 0.7917283 19contrasts(dm$group)## [,1]
## control 1
## stress -1contrasts(dm$shockCond)## [,1]
## safe 1
## threat -1dm %>% filter(!subid %in% subids_lowtrials) %>%
filter(cond == 'sourcehit') %>%
group_by(group,subid) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 21
## 2 stress 19# control=21, stress=19
# just in stress group
summary(lmer(vtc_logit ~ shockCond + (1 + shockCond|subid),
data=dm %>%
filter(!subid %in% subids_lowtrials,
group == 'stress') %>%
filter(cond == 'sourcehit')),
control=lmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5)))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: vtc_logit ~ shockCond + (1 + shockCond | subid)
## Data:
## dm %>% filter(!subid %in% subids_lowtrials, group == "stress") %>%
## filter(cond == "sourcehit")
##
## REML criterion at convergence: 4939.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3289 -0.6154 -0.0290 0.6510 3.2171
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.413890 0.64334
## shockCond1 0.009807 0.09903 1.00
## Residual 8.201927 2.86390
## Number of obs: 994, groups: subid, 19
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.75771 0.17611 18.22000 4.303 0.000418 ***
## shockCond1 -0.06971 0.09413 117.25000 -0.741 0.460467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## shockCond1 0.186# confirm holds with regular ttest -- report this, as straightforward here
dat = dm %>%
filter(!subid %in% subids_lowtrials,
group == 'stress') %>%
filter(cond == 'sourcehit') %>%
group_by(group, subid, shockCond) %>%
summarise(vtc_logit = mean(vtc_logit))
bartlett.test(vtc_logit ~ shockCond, data=dat)##
## Bartlett test of homogeneity of variances
##
## data: vtc_logit by shockCond
## Bartlett's K-squared = 1.3469, df = 1, p-value = 0.2458t.test(vtc_logit ~ shockCond, data=dat, var.equal=TRUE, paired=TRUE)##
## Paired t-test
##
## data: vtc_logit by shockCond
## t = -0.58464, df = 18, p-value = 0.566
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4704082 0.2655937
## sample estimates:
## mean of the differences
## -0.1024073# interaction by group
summary(lmer(vtc_logit ~ shockCond * group + (1 + shockCond|subid),
data=dm %>%
filter(!subid %in% subids_lowtrials) %>%
filter(cond == 'sourcehit')),
control=lmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5))) ## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: vtc_logit ~ shockCond * group + (1 + shockCond | subid)
## Data: dm %>% filter(!subid %in% subids_lowtrials) %>% filter(cond ==
## "sourcehit")
##
## REML criterion at convergence: 13600.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4235 -0.6332 -0.0444 0.6169 4.0419
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 2.931e-01 0.541345
## shockCond1 2.595e-05 0.005094 1.00
## Residual 7.666e+00 2.768736
## Number of obs: 2778, groups: subid, 40
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.615e-01 1.037e-01 3.600e+01 8.309 6.82e-10 ***
## shockCond1 -2.163e-04 5.501e-02 2.678e+03 -0.004 0.997
## group1 1.018e-01 1.037e-01 3.600e+01 0.982 0.333
## shockCond1:group1 6.085e-02 5.501e-02 2.678e+03 1.106 0.269
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) shckC1 group1
## shockCond1 -0.006
## group1 -0.117 0.015
## shckCnd1:g1 0.015 -0.287 -0.006HC assoc hits: scene vs. face/object evidence:
dcat = read.csv('/Users/sgagnon/Dropbox/Stanford/Presentations/AP/mvpa_logit_allcat_byreps_avg_46810_filtartloc_scalewithinrun.csv')
# dcat
dcat = dcat %>%
filter(!subid %in% subids_rm,
cond %in% c('sourcehit')) # rm subjs w/bad localizer
dim(dcat)## [1] 8394 18dcat = dt %>%
mutate(onset=onset_adj, img_type=cond) %>%
dplyr::select(-group, -reps, -cond) %>%
right_join(dcat, by=c('subid', 'run', 'onset')) %>%
filter(shock_and_post == 0)## Warning in right_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vectordim(dcat)## [1] 8391 47# dcat
str(dcat)## 'data.frame': 8391 obs. of 47 variables:
## $ X.x : int 2 2 2 3 3 3 4 4 4 5 ...
## $ run : num 1 1 1 1 1 1 1 1 1 1 ...
## $ trial : int 3 3 3 4 4 4 5 5 5 6 ...
## $ onset : num 19.9 19.9 19.9 29.3 29.3 ...
## $ duration : num 9.42 9.42 9.42 11.34 11.34 ...
## $ shockCond : Factor w/ 2 levels "safe","threat": 1 1 1 1 1 1 1 1 1 1 ...
## $ shockTrial : int 0 0 0 0 0 0 0 0 0 0 ...
## $ target : Factor w/ 252 levels "ACCORDIAN","ACROBAT",..: 238 238 238 116 116 116 114 114 114 183 ...
## $ associate : Factor w/ 241 levels "\002\002","\002",..: 237 237 237 126 126 126 61 61 61 175 ...
## $ resp : Factor w/ 4 levels "foil","indoor",..: 2 2 2 4 4 4 4 4 4 4 ...
## $ acc : Factor w/ 6 levels "CR","FA","H",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ accSpec : Factor w/ 15 levels "CR","FAI_Hi",..: 6 6 6 8 8 8 8 8 8 8 ...
## $ respRT : num 1.68 1.68 1.68 1.73 1.73 ...
## $ subid : chr "ap100" "ap100" "ap100" "ap100" ...
## $ remove : logi NA NA NA NA NA NA ...
## $ anxious : int 3 3 3 3 3 3 3 3 3 3 ...
## $ happy : int 2 2 2 2 2 2 2 2 2 2 ...
## $ safe : int 3 3 3 3 3 3 3 3 3 3 ...
## $ stressed : int 3 3 3 3 3 3 3 3 3 3 ...
## $ life_stress : int 4 4 4 4 4 4 4 4 4 4 ...
## $ sleep : num 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 ...
## $ cond_orig : Factor w/ 5 levels "F_0","TI_2","TI_4",..: 2 2 2 4 4 4 4 4 4 5 ...
## $ shock_and_post : int 0 0 0 0 0 0 0 0 0 0 ...
## $ conf : Factor w/ 3 levels "Hi","Lo","N": 1 1 1 1 1 1 1 1 1 1 ...
## $ onset_adj : num 19.9 19.9 19.9 29.3 29.3 ...
## $ mem_conditions : Factor w/ 7 levels "CR","FA","itemhit_lo",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ obj_conditions : Factor w/ 3 levels "new","nuisance",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ onset_code : Factor w/ 6 levels "CR","FA","itemhit",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ rt_z : num -0.746 -0.746 -0.746 -0.674 -0.674 ...
## $ mem_conditions_byrep: Factor w/ 13 levels "CR-0","FA-0",..: 10 10 10 10 10 10 10 10 10 11 ...
## $ onset_code_byrep : Factor w/ 11 levels "CR-0","FA-0",..: 10 10 10 10 10 10 10 10 10 11 ...
## $ img_type : Factor w/ 3 levels "foil","indoor",..: 2 2 2 3 3 3 3 3 3 3 ...
## $ X.y : int 6 7 8 9 10 11 12 13 14 15 ...
## $ index : int 6 7 8 9 10 11 12 13 14 15 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## $ condition : Factor w/ 10 levels "CR-0","FA-0",..: 7 7 7 7 7 7 7 7 7 8 ...
## $ category : Factor w/ 3 levels "face","object",..: 1 2 3 1 2 3 1 2 3 1 ...
## $ X0 : num 6.81 1.4 -9.26 -1.61 -3.54 ...
## $ X2 : num -0.0374 1.6988 -4.3887 -2.9945 -3.9733 ...
## $ X4 : num -4.72 -2.28 5.793 -3.756 0.899 ...
## $ X6 : num -1.1322 -5.8759 5.6939 0.0628 3.4763 ...
## $ X8 : num -2.776 -5.292 8.645 0.743 0.324 ...
## $ X10 : num -1.05 -3.67 6.49 -7.21 -1.53 ...
## $ X12 : num -2.7 -3.74 6.64 -9.13 -2 ...
## $ avg_logit : num -2.419 -4.279 6.655 -2.54 0.793 ...
## $ cond : Factor w/ 6 levels "CR","FA","itemhit_lo",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ reps : int 2 2 2 2 2 2 2 2 2 4 ...dcat = dcat %>% mutate(subid = factor(subid))
contrasts(dcat$group) = c(1,-1); contrasts(dcat$group)## [,1]
## control 1
## stress -1contrasts(dcat$category) = cbind(sceneVobject=c(0, 1, 0),
sceneVface = c(1, 0, 0)); contrasts(dcat$category)## sceneVobject sceneVface
## face 0 1
## object 1 0
## place 0 0fit = lmer(avg_logit ~ group * category + (1 + category|subid), data=dcat)
anova(fit, refit=FALSE)## Analysis of Variance Table of type III with Satterthwaite
## approximation for degrees of freedom
## Sum Sq Mean Sq NumDF DenDF F.value Pr(>F)
## group 0.01 0.01 1 199.084 0.002 0.9627
## category 912.96 456.48 2 34.784 79.504 1.062e-13 ***
## group:category 15.47 7.74 2 34.784 1.348 0.2731
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(fit)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: avg_logit ~ group * category + (1 + category | subid)
## Data: dcat
##
## REML criterion at convergence: 38614.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0243 -0.6288 -0.0157 0.6055 4.7028
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.3446 0.5871
## categorysceneVobject 0.9569 0.9782 -0.93
## categorysceneVface 0.5892 0.7676 -0.91 0.69
## Residual 5.7416 2.3962
## Number of obs: 8391, groups: subid, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.85473 0.10441 39.16000 8.186 5.18e-10
## group1 0.10611 0.10441 39.16000 1.016 0.316
## categorysceneVobject -1.56620 0.16872 37.93000 -9.283 2.64e-11
## categorysceneVface -1.74503 0.13947 37.21000 -12.512 6.66e-15
## group1:categorysceneVobject -0.25480 0.16872 37.93000 -1.510 0.139
## group1:categorysceneVface -0.06753 0.13947 37.21000 -0.484 0.631
##
## (Intercept) ***
## group1
## categorysceneVobject ***
## categorysceneVface ***
## group1:categorysceneVobject
## group1:categorysceneVface
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 ctgryscnVb ctgryscnVf grp1:ctgryscnVb
## group1 -0.098
## ctgryscnVbj -0.886 0.074
## ctgryscnVfc -0.863 0.079 0.646
## grp1:ctgryscnVb 0.074 -0.886 -0.086 -0.054
## grp1:ctgryscnVf 0.079 -0.863 -0.054 -0.104 0.646HC assoc hits: PPA ~ group
dat = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/pe_ppa_scene_graymid_limitvox.csv')
dat = dat %>% filter(cond %in% c('sourcehit', 'CR')) %>%
mutate(cond = factor(cond))
dat## X cond hemi mask_vox regspace roi
## 1 0 CR bilat 39 epi ppa_scene_graymid_limitvox
## 2 2 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 3 6 CR bilat 39 epi ppa_scene_graymid_limitvox
## 4 8 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 5 12 CR bilat 39 epi ppa_scene_graymid_limitvox
## 6 14 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 7 18 CR bilat 39 epi ppa_scene_graymid_limitvox
## 8 20 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 9 24 CR bilat 39 epi ppa_scene_graymid_limitvox
## 10 26 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 11 30 CR bilat 39 epi ppa_scene_graymid_limitvox
## 12 32 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 13 36 CR bilat 39 epi ppa_scene_graymid_limitvox
## 14 38 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 15 42 CR bilat 39 epi ppa_scene_graymid_limitvox
## 16 44 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 17 48 CR bilat 39 epi ppa_scene_graymid_limitvox
## 18 50 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 19 54 CR bilat 39 epi ppa_scene_graymid_limitvox
## 20 56 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 21 60 CR bilat 39 epi ppa_scene_graymid_limitvox
## 22 62 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 23 66 CR bilat 39 epi ppa_scene_graymid_limitvox
## 24 68 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 25 72 CR bilat 39 epi ppa_scene_graymid_limitvox
## 26 74 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 27 78 CR bilat 39 epi ppa_scene_graymid_limitvox
## 28 80 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 29 84 CR bilat 39 epi ppa_scene_graymid_limitvox
## 30 86 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 31 90 CR bilat 39 epi ppa_scene_graymid_limitvox
## 32 92 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 33 96 CR bilat 39 epi ppa_scene_graymid_limitvox
## 34 98 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 35 102 CR bilat 39 epi ppa_scene_graymid_limitvox
## 36 104 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 37 108 CR bilat 39 epi ppa_scene_graymid_limitvox
## 38 110 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 39 114 CR bilat 39 epi ppa_scene_graymid_limitvox
## 40 116 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 41 120 CR bilat 39 epi ppa_scene_graymid_limitvox
## 42 122 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 43 126 CR bilat 39 epi ppa_scene_graymid_limitvox
## 44 128 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 45 132 CR bilat 39 epi ppa_scene_graymid_limitvox
## 46 134 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 47 138 CR bilat 39 epi ppa_scene_graymid_limitvox
## 48 140 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 49 144 CR bilat 39 epi ppa_scene_graymid_limitvox
## 50 146 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 51 150 CR bilat 39 epi ppa_scene_graymid_limitvox
## 52 152 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 53 156 CR bilat 39 epi ppa_scene_graymid_limitvox
## 54 158 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 55 162 CR bilat 39 epi ppa_scene_graymid_limitvox
## 56 164 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 57 168 CR bilat 39 epi ppa_scene_graymid_limitvox
## 58 170 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 59 174 CR bilat 39 epi ppa_scene_graymid_limitvox
## 60 176 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 61 180 CR bilat 39 epi ppa_scene_graymid_limitvox
## 62 182 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 63 186 CR bilat 39 epi ppa_scene_graymid_limitvox
## 64 188 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 65 192 CR bilat 39 epi ppa_scene_graymid_limitvox
## 66 194 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 67 198 CR bilat 39 epi ppa_scene_graymid_limitvox
## 68 200 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 69 204 CR bilat 39 epi ppa_scene_graymid_limitvox
## 70 206 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 71 210 CR bilat 39 epi ppa_scene_graymid_limitvox
## 72 212 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 73 216 CR bilat 39 epi ppa_scene_graymid_limitvox
## 74 218 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 75 222 CR bilat 39 epi ppa_scene_graymid_limitvox
## 76 224 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 77 228 CR bilat 39 epi ppa_scene_graymid_limitvox
## 78 230 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 79 234 CR bilat 39 epi ppa_scene_graymid_limitvox
## 80 236 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 81 240 CR bilat 39 epi ppa_scene_graymid_limitvox
## 82 242 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 83 246 CR bilat 39 epi ppa_scene_graymid_limitvox
## 84 248 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 85 252 CR bilat 39 epi ppa_scene_graymid_limitvox
## 86 254 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## 87 258 CR bilat 39 epi ppa_scene_graymid_limitvox
## 88 260 sourcehit bilat 39 epi ppa_scene_graymid_limitvox
## smoothing subid value group
## 1 unsmoothed ap100 0.2781278 control
## 2 unsmoothed ap100 2524.3881836 control
## 3 unsmoothed ap101 63.5418472 control
## 4 unsmoothed ap101 1671.2398682 control
## 5 unsmoothed ap102 -2253.8598633 control
## 6 unsmoothed ap102 754.4208984 control
## 7 unsmoothed ap103 -1441.9023438 control
## 8 unsmoothed ap103 991.7300415 control
## 9 unsmoothed ap104 -1840.0306397 control
## 10 unsmoothed ap104 420.6114807 control
## 11 unsmoothed ap105 203.5516205 control
## 12 unsmoothed ap105 1903.8507080 control
## 13 unsmoothed ap107 -767.6729736 control
## 14 unsmoothed ap107 567.1591797 control
## 15 unsmoothed ap108 -101.2654343 control
## 16 unsmoothed ap108 367.3101501 control
## 17 unsmoothed ap109 -394.9261169 control
## 18 unsmoothed ap109 678.5880127 control
## 19 unsmoothed ap110 -1345.3963623 control
## 20 unsmoothed ap110 -322.4353943 control
## 21 unsmoothed ap111 -1456.6593018 control
## 22 unsmoothed ap111 945.7440796 control
## 23 unsmoothed ap113 -137.1090851 control
## 24 unsmoothed ap113 896.7670288 control
## 25 unsmoothed ap114 372.9386291 control
## 26 unsmoothed ap114 1637.6826172 control
## 27 unsmoothed ap115 -803.8724365 control
## 28 unsmoothed ap115 1405.4901123 control
## 29 unsmoothed ap116 326.8256531 control
## 30 unsmoothed ap116 3313.5002441 control
## 31 unsmoothed ap117 -220.4937134 control
## 32 unsmoothed ap117 2175.1704102 control
## 33 unsmoothed ap118 -1649.9655762 control
## 34 unsmoothed ap118 1116.4371338 control
## 35 unsmoothed ap119 -1142.4383545 control
## 36 unsmoothed ap119 1316.4415283 control
## 37 unsmoothed ap120 -245.4828186 control
## 38 unsmoothed ap120 1640.0705566 control
## 39 unsmoothed ap121 288.2007141 control
## 40 unsmoothed ap121 65.7652969 control
## 41 unsmoothed ap122 -1537.2353516 control
## 42 unsmoothed ap122 1111.7022705 control
## 43 unsmoothed ap150 656.7203369 stress
## 44 unsmoothed ap150 1457.1075440 stress
## 45 unsmoothed ap152 -187.3688965 stress
## 46 unsmoothed ap152 707.0936890 stress
## 47 unsmoothed ap153 -57.7346115 stress
## 48 unsmoothed ap153 604.3408813 stress
## 49 unsmoothed ap154 -207.6078186 stress
## 50 unsmoothed ap154 771.2774658 stress
## 51 unsmoothed ap155 -63.5730591 stress
## 52 unsmoothed ap155 594.9816895 stress
## 53 unsmoothed ap157 -1069.8568115 stress
## 54 unsmoothed ap157 297.9955139 stress
## 55 unsmoothed ap158 194.7604370 control
## 56 unsmoothed ap158 1793.5716553 control
## 57 unsmoothed ap159 -139.6088867 stress
## 58 unsmoothed ap159 1568.5673828 stress
## 59 unsmoothed ap160 -938.8587646 stress
## 60 unsmoothed ap160 297.3102112 stress
## 61 unsmoothed ap161 -594.2133179 stress
## 62 unsmoothed ap161 -588.5639038 stress
## 63 unsmoothed ap162 -358.7315063 stress
## 64 unsmoothed ap162 675.8694458 stress
## 65 unsmoothed ap164 -2545.5439453 stress
## 66 unsmoothed ap164 1764.1096191 stress
## 67 unsmoothed ap163 -213.9288788 stress
## 68 unsmoothed ap163 2066.4916992 stress
## 69 unsmoothed ap165 -1023.0679321 stress
## 70 unsmoothed ap165 910.4725342 stress
## 71 unsmoothed ap166 72.8014145 stress
## 72 unsmoothed ap166 2252.0588379 stress
## 73 unsmoothed ap167 -159.6408691 stress
## 74 unsmoothed ap167 1698.9511719 stress
## 75 unsmoothed ap168 -534.8930664 stress
## 76 unsmoothed ap168 -792.1364746 stress
## 77 unsmoothed ap169 3.8843024 stress
## 78 unsmoothed ap169 856.3543701 stress
## 79 unsmoothed ap170 -347.3394165 stress
## 80 unsmoothed ap170 1608.4581299 stress
## 81 unsmoothed ap171 28.4471684 stress
## 82 unsmoothed ap171 1583.1534424 stress
## 83 unsmoothed ap172 -335.4015503 stress
## 84 unsmoothed ap172 23.6653557 stress
## 85 unsmoothed ap173 21.6861172 stress
## 86 unsmoothed ap173 991.1500244 stress
## 87 unsmoothed ap174 160.3838501 stress
## 88 unsmoothed ap174 1794.9483643 stressbartlett.test(value ~ group, data=dat %>% filter(cond == 'sourcehit'))##
## Bartlett test of homogeneity of variances
##
## data: value by group
## Bartlett's K-squared = 0.015018, df = 1, p-value = 0.9025t.test(value ~ group, data=dat %>% filter(cond == 'sourcehit'), var.equal=TRUE)##
## Two Sample t-test
##
## data: value by group
## t = 1.0664, df = 42, p-value = 0.2923
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -236.5547 766.6955
## sample estimates:
## mean in group control mean in group stress
## 1226.1457 961.0753t.test(value ~ group, data=dat %>% filter(cond == 'CR'), var.equal=TRUE)##
## Two Sample t-test
##
## data: value by group
## t = -1.2486, df = 42, p-value = 0.2187
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -720.0389 169.6055
## sample estimates:
## mean in group control mean in group stress
## -631.2824 -356.0657# sig interaction? might be driven by decrease in CRs in ctrl, and directional increase in sh
contrasts(dat$group) = c(1,-1); contrasts(dat$group)## [,1]
## control 1
## stress -1contrasts(dat$cond) = c(-1,1); contrasts(dat$cond)## [,1]
## CR -1
## sourcehit 1summary(lmer(value ~ group * cond + (1|subid), data=dat)) # rand slope, not enough obs## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ group * cond + (1 | subid)
## Data: dat
##
## REML criterion at convergence: 1370
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8955 -0.5112 0.1508 0.4783 2.0861
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 201441 448.8
## Residual 405588 636.9
## Number of obs: 88, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 299.968 95.850 42.000 3.130 0.00318 **
## group1 -2.537 95.850 42.000 -0.026 0.97901
## cond1 793.642 67.889 43.310 11.690 5.55e-15 ***
## group1:cond1 135.072 67.889 43.310 1.990 0.05297 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 cond1
## group1 0.000
## cond1 0.000 0.000
## group1:cnd1 0.000 0.000 0.000Visualize timecourse of reinstatement
by group
head(dm)## # A tibble: 6 x 83
## X.x run trial onset duration shockCond shockTrial target
## <int> <dbl> <int> <dbl> <dbl> <fctr> <int> <fctr>
## 1 0 1 1 0.0191 10.6661 safe 0 BANDIT
## 2 2 1 3 19.8642 9.4224 safe 0 VIOLIN
## 3 3 1 4 29.3132 11.3389 safe 0 MEDAL
## 4 4 1 5 40.6786 9.7644 safe 0 MANURE
## 5 5 1 6 50.4701 9.1180 safe 0 SARDINE
## 6 7 1 8 69.7414 9.9338 safe 0 TOBACCO
## # ... with 75 more variables: associate <fctr>, resp <fctr>, acc <fctr>,
## # accSpec <fctr>, respRT <dbl>, subid <fctr>, remove <lgl>,
## # anxious <int>, happy <int>, safe <int>, stressed <int>,
## # life_stress <int>, sleep <dbl>, cond_orig <fctr>,
## # shock_and_post <int>, conf <fctr>, onset_adj <dbl>,
## # mem_conditions <fctr>, obj_conditions <fctr>, onset_code <fctr>,
## # rt_z <dbl>, mem_conditions_byrep <fctr>, onset_code_byrep <fctr>,
## # img_type <fctr>, X.y <int>, index <int>, group <fctr>,
## # condition <fctr>, category <fctr>, X0 <dbl>, X2 <dbl>, X4 <dbl>,
## # X6 <dbl>, X8 <dbl>, X10 <dbl>, X12 <dbl>, avg_logit <dbl>,
## # cond <fctr>, reps <fctr>, pcorr <fctr>, vtc_logit <dbl>,
## # imgType <fctr>, hipp_sig <dbl>, hipp_tail_sig <dbl>, DAN_sig <dbl>,
## # CCN_sig <dbl>, rsp_sig <dbl>, angular_lh_sig <dbl>, DAN_lh_sig <dbl>,
## # CCN_lh_sig <dbl>, ang_logit <dbl>, hipp_logit <dbl>,
## # vtc_nophc_logit <dbl>, ang_logit_z <dbl>, vtc_logit_z <dbl>,
## # hipp_logit_z <dbl>, CCN_sig_z <dbl>, rsp_sig_z <dbl>, DAN_sig_z <dbl>,
## # angular_lh_sig_z <dbl>, CCN_lh_sig_z <dbl>, DAN_lh_sig_z <dbl>,
## # hipp_sig_z <dbl>, hipp_tail_sig_z <dbl>, hipp_quintile <int>,
## # rsp_quintile <int>, CCN_quintile <int>, DAN_quintile <int>,
## # angular_lh_sig_quintile <int>, CCN_lh_quintile <int>,
## # DAN_lh_quintile <int>, hipp_tail_quintile <int>, vtc_quintile <int>,
## # hipp_logit_quintile <int>, ang_quintile <int>dm_time = dm %>%
filter(cond %in% c('sourcehit')) %>%
gather('time', 'logit_vtc', `X0`:`X12`) %>%
mutate(time=gsub("X","",time)) %>%
group_by(group, subid, cond, time) %>%
summarise(mean_logit = mean_(logit_vtc)) %>%
group_by(group, cond, time) %>%
summarise(mean=mean_(mean_logit), sem=std.error(mean_logit), n=length(mean_logit)) %>%
mutate(time = as.numeric(time))
p2 = ggplot(dm_time %>% filter(cond == 'sourcehit'),
aes(x=factor(time), y=mean, group=group, color=group)) +
geom_line(position=position_dodge(.2), size=1.5) +
geom_point(position=position_dodge(.2), size=5) +
geom_errorbar(width=0, aes(ymin=mean-sem,
ymax=mean+sem), size=1.5,
position=position_dodge(.2)) +
scale_color_manual(values = palette,
guide = guide_legend(title = NULL)) +
ylab('Reinstatement strength\n(logits)') +
xlab('Time post-stimulus onset (s)') +
theme(legend.title=element_blank(),
legend.position = c(.9, .8))
ggsave('~/Experiments/AP/figs/AP_reinstatement_bygroup_SH_ts.jpeg',
dpi=150, width=5.5, height=4)
p2
## no dodge
p2 = ggplot(dm_time %>% filter(cond == 'sourcehit'),
aes(x=factor(time), y=mean, group=group, color=group)) +
geom_line(size=1.5) +
geom_point(size=5) +
geom_errorbar(width=0, aes(ymin=mean-sem,
ymax=mean+sem), size=1.5, alpha=.65) +
scale_color_manual(values = palette,
guide = guide_legend(title = NULL)) +
ylab('Reinstatement strength') +
xlab('Time post-stimulus onset (s)') +
theme(legend.title=element_blank(),
legend.position = c(.9, .8))
p2
ggsave('~/Experiments/AP/figs/AP_reinstatement_bygroup_SH_ts_nododge.jpeg',
dpi=600, width=5, height=4)
p2
HC recollection: by encoding strength * group
subids_lowtrials = dm %>%
filter(cond == 'sourcehit') %>%
group_by(group, subid, reps) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), reps, fill = list(count= 0)) %>%
filter(count < 6) %>% pull(subid) %>% unique()
subids_lowtrials## [1] ap110 ap158 ap173
## 42 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ... ap173with(dm %>%
filter(cond %in% c('sourcehit')),
table(subid, reps))## reps
## subid 2 4
## ap100 38 49
## ap101 37 65
## ap102 23 43
## ap103 48 63
## ap104 67 79
## ap105 49 68
## ap107 60 62
## ap108 12 16
## ap109 20 38
## ap110 3 17
## ap111 23 50
## ap113 28 47
## ap114 43 58
## ap115 32 45
## ap116 69 75
## ap117 36 48
## ap118 21 41
## ap119 44 68
## ap120 29 42
## ap121 16 17
## ap122 40 55
## ap150 28 38
## ap152 15 25
## ap153 7 24
## ap154 14 28
## ap155 18 20
## ap157 13 38
## ap158 2 9
## ap159 21 29
## ap160 23 28
## ap161 9 18
## ap162 19 22
## ap163 48 59
## ap164 28 53
## ap165 28 36
## ap166 27 44
## ap167 14 24
## ap169 33 36
## ap170 27 47
## ap171 9 24
## ap172 9 11
## ap173 2 6with(dm %>%
filter(!subid %in% subids_lowtrials,
cond %in% c('sourcehit')),
table(subid, reps))## reps
## subid 2 4
## ap100 38 49
## ap101 37 65
## ap102 23 43
## ap103 48 63
## ap104 67 79
## ap105 49 68
## ap107 60 62
## ap108 12 16
## ap109 20 38
## ap110 0 0
## ap111 23 50
## ap113 28 47
## ap114 43 58
## ap115 32 45
## ap116 69 75
## ap117 36 48
## ap118 21 41
## ap119 44 68
## ap120 29 42
## ap121 16 17
## ap122 40 55
## ap150 28 38
## ap152 15 25
## ap153 7 24
## ap154 14 28
## ap155 18 20
## ap157 13 38
## ap158 0 0
## ap159 21 29
## ap160 23 28
## ap161 9 18
## ap162 19 22
## ap163 48 59
## ap164 28 53
## ap165 28 36
## ap166 27 44
## ap167 14 24
## ap169 33 36
## ap170 27 47
## ap171 9 24
## ap172 9 11
## ap173 0 0dm_time = dm %>%
filter(!subid %in% subids_lowtrials) %>%
filter(cond %in% c('sourcehit')) %>%
gather('time', 'logit_vtc', `X0`:`X12`) %>%
mutate(time=gsub("X","",time)) %>%
group_by(group, subid, reps, time) %>%
summarise(mean_logit = mean_(logit_vtc)) %>%
group_by(group, reps, time) %>%
summarise(mean=mean_(mean_logit), sem=std.error(mean_logit), n=length(mean_logit)) %>%
mutate(time = as.numeric(time)) %>%
ungroup() %>%
mutate(reps = revalue(reps, replace = c('2' = 'weak',
'4' = 'strong'))) %>%
mutate(group = revalue(group, replace = c('control' = 'Control',
'stress' = 'Stress')))
dm_time## # A tibble: 28 x 6
## group reps time mean sem n
## <fctr> <fctr> <dbl> <dbl> <dbl> <int>
## 1 Control weak 0 -0.32485895 0.24148422 20
## 2 Control weak 10 -0.03617768 0.18297077 20
## 3 Control weak 12 -0.31620502 0.17325071 20
## 4 Control weak 2 -0.35822065 0.18152562 20
## 5 Control weak 4 0.53657947 0.14192816 20
## 6 Control weak 6 1.70658865 0.28438341 20
## 7 Control weak 8 1.17585111 0.24646744 20
## 8 Control strong 0 -0.11328754 0.09725629 20
## 9 Control strong 10 0.08498864 0.16953268 20
## 10 Control strong 12 -0.05039954 0.17108371 20
## # ... with 18 more rowsp2 = ggplot(dm_time,
aes(x=factor(time), y=mean, group=reps, color=reps)) +
geom_line(position=position_dodge(.2), size=1.5) +
geom_point(position=position_dodge(.2), size=5) +
facet_grid(.~group)+
geom_errorbar(width=0, aes(ymin=mean-sem,
ymax=mean+sem), size=1.5,
position=position_dodge(.2)) +
scale_color_brewer(palette = 'Set1',
guide = guide_legend(title = NULL)) +
ylab('Reinstatement strength\n(logits)') +
xlab('Time post-stimulus onset (s)') +
theme_classic(base_size = 18)+
theme(legend.position = c(.5, .8),
legend.title=element_blank(),
strip.text = element_text(size=18),
strip.background = element_rect(colour="white", fill="white"))
p2
ggsave('~/Experiments/AP/figs/AP_reinstatement_bygroupXrep_SH_ts.jpeg',
dpi=300, width=8, height=4)
ggsave('~/Dropbox/Stanford/Papers/AP/Figures/AP_supp_figure6.tiff',
dpi=600, width=8, height=4)
ggsave('~/Dropbox/Stanford/Papers/AP/Figures/AP_supp_figure6_150dpi.tiff',
dpi=150, width=8, height=4)
p2
All old conditions
head(dm)## # A tibble: 6 x 83
## X.x run trial onset duration shockCond shockTrial target
## <int> <dbl> <int> <dbl> <dbl> <fctr> <int> <fctr>
## 1 0 1 1 0.0191 10.6661 safe 0 BANDIT
## 2 2 1 3 19.8642 9.4224 safe 0 VIOLIN
## 3 3 1 4 29.3132 11.3389 safe 0 MEDAL
## 4 4 1 5 40.6786 9.7644 safe 0 MANURE
## 5 5 1 6 50.4701 9.1180 safe 0 SARDINE
## 6 7 1 8 69.7414 9.9338 safe 0 TOBACCO
## # ... with 75 more variables: associate <fctr>, resp <fctr>, acc <fctr>,
## # accSpec <fctr>, respRT <dbl>, subid <fctr>, remove <lgl>,
## # anxious <int>, happy <int>, safe <int>, stressed <int>,
## # life_stress <int>, sleep <dbl>, cond_orig <fctr>,
## # shock_and_post <int>, conf <fctr>, onset_adj <dbl>,
## # mem_conditions <fctr>, obj_conditions <fctr>, onset_code <fctr>,
## # rt_z <dbl>, mem_conditions_byrep <fctr>, onset_code_byrep <fctr>,
## # img_type <fctr>, X.y <int>, index <int>, group <fctr>,
## # condition <fctr>, category <fctr>, X0 <dbl>, X2 <dbl>, X4 <dbl>,
## # X6 <dbl>, X8 <dbl>, X10 <dbl>, X12 <dbl>, avg_logit <dbl>,
## # cond <fctr>, reps <fctr>, pcorr <fctr>, vtc_logit <dbl>,
## # imgType <fctr>, hipp_sig <dbl>, hipp_tail_sig <dbl>, DAN_sig <dbl>,
## # CCN_sig <dbl>, rsp_sig <dbl>, angular_lh_sig <dbl>, DAN_lh_sig <dbl>,
## # CCN_lh_sig <dbl>, ang_logit <dbl>, hipp_logit <dbl>,
## # vtc_nophc_logit <dbl>, ang_logit_z <dbl>, vtc_logit_z <dbl>,
## # hipp_logit_z <dbl>, CCN_sig_z <dbl>, rsp_sig_z <dbl>, DAN_sig_z <dbl>,
## # angular_lh_sig_z <dbl>, CCN_lh_sig_z <dbl>, DAN_lh_sig_z <dbl>,
## # hipp_sig_z <dbl>, hipp_tail_sig_z <dbl>, hipp_quintile <int>,
## # rsp_quintile <int>, CCN_quintile <int>, DAN_quintile <int>,
## # angular_lh_sig_quintile <int>, CCN_lh_quintile <int>,
## # DAN_lh_quintile <int>, hipp_tail_quintile <int>, vtc_quintile <int>,
## # hipp_logit_quintile <int>, ang_quintile <int>dm_time = dm %>%
gather('time', 'logit_vtc', `X0`:`X12`) %>%
mutate(time=gsub("X","",time)) %>%
group_by(group, subid, cond, time) %>%
summarise(mean_logit = mean_(logit_vtc)) %>%
group_by(group, cond, time) %>%
summarise(mean=mean_(mean_logit),
sem=std.error(mean_logit),
n=length(mean_logit)) %>%
mutate(time = as.numeric(time))
# update names for plot
unique(dm_time$cond)## [1] itemhit_lo M sourcehit sourcemiss_hi
## Levels: itemhit_lo M sourcehit sourcemiss_hidm_time = dm_time %>% ungroup() %>%
mutate(cond = as.character(cond),
cond = revalue(cond, replace = c('sourcehit' = 'HC assoc hit',
'sourcemiss_hi' = 'HC assoc FA',
'itemhit_lo' = 'LC item hit',
'M' = 'Miss')),
cond = factor(cond, levels=c('HC assoc hit',
'HC assoc FA',
'LC item hit',
'Miss')))
dm_time## # A tibble: 56 x 6
## group cond time mean sem n
## <fctr> <fctr> <dbl> <dbl> <dbl> <int>
## 1 control LC item hit 0 -0.36392494 0.1858448 21
## 2 control LC item hit 10 0.63963679 0.3057431 21
## 3 control LC item hit 12 0.13772789 0.2791502 21
## 4 control LC item hit 2 -0.23660316 0.2516772 21
## 5 control LC item hit 4 -0.16198037 0.1261915 21
## 6 control LC item hit 6 -0.54277399 0.1894866 21
## 7 control LC item hit 8 -0.07878289 0.2009770 21
## 8 control Miss 0 -0.16964263 0.2188713 22
## 9 control Miss 10 -0.78060636 0.2556924 22
## 10 control Miss 12 -0.57912127 0.2478504 22
## # ... with 46 more rowsp2 = ggplot(dm_time,
aes(x=factor(time), y=mean, group=cond, color=cond)) +
facet_grid(.~group)+
geom_errorbar(width=0, aes(ymin=mean-sem,
ymax=mean+sem), size=1.5,
position=position_dodge(.5)) +
geom_line(position=position_dodge(.5), size=1.5) +
geom_point(position=position_dodge(.5), size=5) +
scale_color_brewer(palette = 'RdYlBu',
guide = guide_legend(title = NULL)) +
ylab('Reinstatement strength\n(logits)') +
xlab('Time post-stimulus onset (s)') +
theme(legend.title=element_blank())
p2
ggsave('~/Experiments/AP/figs/AP_reinstatement_bygroup_allOLD_ts.jpeg',
dpi=150, width=9, height=4)
p2 ### All old conditions by hipp quintile
head(dm)## # A tibble: 6 x 83
## X.x run trial onset duration shockCond shockTrial target
## <int> <dbl> <int> <dbl> <dbl> <fctr> <int> <fctr>
## 1 0 1 1 0.0191 10.6661 safe 0 BANDIT
## 2 2 1 3 19.8642 9.4224 safe 0 VIOLIN
## 3 3 1 4 29.3132 11.3389 safe 0 MEDAL
## 4 4 1 5 40.6786 9.7644 safe 0 MANURE
## 5 5 1 6 50.4701 9.1180 safe 0 SARDINE
## 6 7 1 8 69.7414 9.9338 safe 0 TOBACCO
## # ... with 75 more variables: associate <fctr>, resp <fctr>, acc <fctr>,
## # accSpec <fctr>, respRT <dbl>, subid <fctr>, remove <lgl>,
## # anxious <int>, happy <int>, safe <int>, stressed <int>,
## # life_stress <int>, sleep <dbl>, cond_orig <fctr>,
## # shock_and_post <int>, conf <fctr>, onset_adj <dbl>,
## # mem_conditions <fctr>, obj_conditions <fctr>, onset_code <fctr>,
## # rt_z <dbl>, mem_conditions_byrep <fctr>, onset_code_byrep <fctr>,
## # img_type <fctr>, X.y <int>, index <int>, group <fctr>,
## # condition <fctr>, category <fctr>, X0 <dbl>, X2 <dbl>, X4 <dbl>,
## # X6 <dbl>, X8 <dbl>, X10 <dbl>, X12 <dbl>, avg_logit <dbl>,
## # cond <fctr>, reps <fctr>, pcorr <fctr>, vtc_logit <dbl>,
## # imgType <fctr>, hipp_sig <dbl>, hipp_tail_sig <dbl>, DAN_sig <dbl>,
## # CCN_sig <dbl>, rsp_sig <dbl>, angular_lh_sig <dbl>, DAN_lh_sig <dbl>,
## # CCN_lh_sig <dbl>, ang_logit <dbl>, hipp_logit <dbl>,
## # vtc_nophc_logit <dbl>, ang_logit_z <dbl>, vtc_logit_z <dbl>,
## # hipp_logit_z <dbl>, CCN_sig_z <dbl>, rsp_sig_z <dbl>, DAN_sig_z <dbl>,
## # angular_lh_sig_z <dbl>, CCN_lh_sig_z <dbl>, DAN_lh_sig_z <dbl>,
## # hipp_sig_z <dbl>, hipp_tail_sig_z <dbl>, hipp_quintile <int>,
## # rsp_quintile <int>, CCN_quintile <int>, DAN_quintile <int>,
## # angular_lh_sig_quintile <int>, CCN_lh_quintile <int>,
## # DAN_lh_quintile <int>, hipp_tail_quintile <int>, vtc_quintile <int>,
## # hipp_logit_quintile <int>, ang_quintile <int>dm_time = dm %>%
gather('time', 'logit_vtc', `X0`:`X12`) %>%
mutate(time=gsub("X","",time)) %>%
group_by(group, subid, cond, time, hipp_quintile) %>%
summarise(mean_logit = mean_(logit_vtc)) %>%
group_by(group, cond, time, hipp_quintile) %>%
summarise(mean=mean_(mean_logit),
sem=std.error(mean_logit),
n=length(mean_logit)) %>%
ungroup() %>%
mutate(time = as.numeric(time))
# update names for plot
unique(dm_time$cond)## [1] itemhit_lo M sourcehit sourcemiss_hi
## Levels: itemhit_lo M sourcehit sourcemiss_hidm_time = dm_time %>% ungroup() %>%
mutate(cond = as.character(cond),
cond = revalue(cond, replace = c('sourcehit' = 'HC assoc hit',
'sourcemiss_hi' = 'HC assoc FA',
'itemhit_lo' = 'LC item hit',
'M' = 'Miss')),
cond = factor(cond, levels=c('HC assoc hit',
'HC assoc FA',
'LC item hit',
'Miss')))
dm_time## # A tibble: 280 x 7
## group cond time hipp_quintile mean sem n
## <fctr> <fctr> <dbl> <int> <dbl> <dbl> <int>
## 1 control LC item hit 0 1 -0.1617156 0.2308486 21
## 2 control LC item hit 0 2 -0.1324871 0.2991220 20
## 3 control LC item hit 0 3 -0.1092320 0.3948682 20
## 4 control LC item hit 0 4 -1.0374211 0.4837502 20
## 5 control LC item hit 0 5 -0.6314236 0.6985017 19
## 6 control LC item hit 10 1 0.3893503 0.3336490 21
## 7 control LC item hit 10 2 0.5862498 0.3381496 20
## 8 control LC item hit 10 3 0.4088204 0.3360453 20
## 9 control LC item hit 10 4 0.9625374 0.6375035 20
## 10 control LC item hit 10 5 1.3195959 0.5829748 19
## # ... with 270 more rowsp2 = ggplot(dm_time,
aes(x=factor(time), y=mean, group=cond, color=cond)) +
facet_grid(group~hipp_quintile)+
geom_errorbar(width=0, aes(ymin=mean-sem,
ymax=mean+sem), size=1.5,
position=position_dodge(.5)) +
geom_line(position=position_dodge(.5), size=1.5) +
geom_point(position=position_dodge(.5), size=5) +
scale_color_brewer(palette = 'RdYlBu',
guide = guide_legend(title = NULL)) +
ylab('Reinstatement strength\n(logits)') +
xlab('Time post-stimulus onset (s)') +
theme(legend.title=element_blank())
p2
ggsave('~/Experiments/AP/figs/AP_reinstatement_bygroupXhippquintile_allOLD_ts.jpeg',
dpi=150, width=20, height=9)
p2
p2 = ggplot(dm_time,
aes(x=factor(time), y=mean, group=factor(hipp_quintile), color=factor(hipp_quintile))) +
facet_grid(group~cond)+
geom_hline(yintercept = 0, linetype=2)+
geom_errorbar(width=0, aes(ymin=mean-sem,
ymax=mean+sem), size=1.5,
position=position_dodge(.5)) +
geom_line(position=position_dodge(.5), size=1.5) +
geom_point(position=position_dodge(.5), size=5) +
scale_color_brewer(palette = 'RdYlBu',
guide = guide_legend(title = NULL)) +
ylab('Reinstatement strength\n(logits)') +
xlab('Time post-stimulus onset (s)') +
theme(legend.title=element_blank())
ggsave('~/Experiments/AP/figs/AP_reinstatement_bygroupXhippquintile_allOLD_ts_v2.jpeg',
dpi=150, width=20, height=9)
p2
p2 = ggplot(dm_time,
aes(x=factor(time), y=mean, group=group, color=group)) +
facet_grid(cond~hipp_quintile)+
geom_hline(yintercept = 0, linetype=2)+
geom_errorbar(width=0, aes(ymin=mean-sem,
ymax=mean+sem), size=1.5,
position=position_dodge(.5)) +
geom_line(position=position_dodge(.5), size=1.5) +
geom_point(position=position_dodge(.5), size=5) +
scale_color_manual(values=palette,
guide = guide_legend(title = NULL)) +
ylab('Reinstatement strength\n(logits)') +
xlab('Time post-stimulus onset (s)') +
theme(legend.title=element_blank())
p2
ggsave('~/Experiments/AP/figs/AP_reinstatement_bygroupXhippquintile_allOLD_ts_v3.jpeg',
dpi=150, width=15, height=15)
p2
head(dm)## # A tibble: 6 x 83
## X.x run trial onset duration shockCond shockTrial target
## <int> <dbl> <int> <dbl> <dbl> <fctr> <int> <fctr>
## 1 0 1 1 0.0191 10.6661 safe 0 BANDIT
## 2 2 1 3 19.8642 9.4224 safe 0 VIOLIN
## 3 3 1 4 29.3132 11.3389 safe 0 MEDAL
## 4 4 1 5 40.6786 9.7644 safe 0 MANURE
## 5 5 1 6 50.4701 9.1180 safe 0 SARDINE
## 6 7 1 8 69.7414 9.9338 safe 0 TOBACCO
## # ... with 75 more variables: associate <fctr>, resp <fctr>, acc <fctr>,
## # accSpec <fctr>, respRT <dbl>, subid <fctr>, remove <lgl>,
## # anxious <int>, happy <int>, safe <int>, stressed <int>,
## # life_stress <int>, sleep <dbl>, cond_orig <fctr>,
## # shock_and_post <int>, conf <fctr>, onset_adj <dbl>,
## # mem_conditions <fctr>, obj_conditions <fctr>, onset_code <fctr>,
## # rt_z <dbl>, mem_conditions_byrep <fctr>, onset_code_byrep <fctr>,
## # img_type <fctr>, X.y <int>, index <int>, group <fctr>,
## # condition <fctr>, category <fctr>, X0 <dbl>, X2 <dbl>, X4 <dbl>,
## # X6 <dbl>, X8 <dbl>, X10 <dbl>, X12 <dbl>, avg_logit <dbl>,
## # cond <fctr>, reps <fctr>, pcorr <fctr>, vtc_logit <dbl>,
## # imgType <fctr>, hipp_sig <dbl>, hipp_tail_sig <dbl>, DAN_sig <dbl>,
## # CCN_sig <dbl>, rsp_sig <dbl>, angular_lh_sig <dbl>, DAN_lh_sig <dbl>,
## # CCN_lh_sig <dbl>, ang_logit <dbl>, hipp_logit <dbl>,
## # vtc_nophc_logit <dbl>, ang_logit_z <dbl>, vtc_logit_z <dbl>,
## # hipp_logit_z <dbl>, CCN_sig_z <dbl>, rsp_sig_z <dbl>, DAN_sig_z <dbl>,
## # angular_lh_sig_z <dbl>, CCN_lh_sig_z <dbl>, DAN_lh_sig_z <dbl>,
## # hipp_sig_z <dbl>, hipp_tail_sig_z <dbl>, hipp_quintile <int>,
## # rsp_quintile <int>, CCN_quintile <int>, DAN_quintile <int>,
## # angular_lh_sig_quintile <int>, CCN_lh_quintile <int>,
## # DAN_lh_quintile <int>, hipp_tail_quintile <int>, vtc_quintile <int>,
## # hipp_logit_quintile <int>, ang_quintile <int>dm_time = dm %>%
group_by(group, subid, cond, hipp_quintile) %>%
summarise(mean_logit = mean_(vtc_logit)) %>%
group_by(group, cond, hipp_quintile) %>%
summarise(mean=mean_(mean_logit),
sem=std.error(mean_logit),
n=length(mean_logit)) %>%
ungroup()
# update names for plot
dm_time = dm_time %>% ungroup() %>%
mutate(cond = as.character(cond),
cond = revalue(cond, replace = c('sourcehit' = 'HC assoc hit',
'sourcemiss_hi' = 'HC assoc FA',
'itemhit_lo' = 'LC item hit',
'M' = 'Miss')),
cond = factor(cond, levels=c('HC assoc hit',
'HC assoc FA',
'LC item hit',
'Miss')))
dm_time## # A tibble: 40 x 6
## group cond hipp_quintile mean sem n
## <fctr> <fctr> <int> <dbl> <dbl> <int>
## 1 control LC item hit 1 -0.38368700 0.2592430 21
## 2 control LC item hit 2 -0.25445570 0.2815357 20
## 3 control LC item hit 3 -0.02095554 0.1722509 20
## 4 control LC item hit 4 0.42818584 0.2498491 20
## 5 control LC item hit 5 1.03541320 0.3938372 19
## 6 control Miss 1 -2.28335646 0.3236401 22
## 7 control Miss 2 -1.79483084 0.3340072 21
## 8 control Miss 3 -1.10737456 0.3871727 21
## 9 control Miss 4 -0.44900746 0.3308352 19
## 10 control Miss 5 0.36367873 0.4300350 17
## # ... with 30 more rowsp2 = ggplot(dm_time,
aes(x=factor(hipp_quintile), y=mean, group=group, color=group)) +
facet_grid(.~cond)+
geom_hline(yintercept = 0, linetype=2)+
geom_errorbar(width=0, aes(ymin=mean-sem,
ymax=mean+sem), size=1.5,
position=position_dodge(.5)) +
geom_line(position=position_dodge(.5), size=1.5) +
geom_point(position=position_dodge(.5), size=5) +
scale_color_manual(values=palette,
guide = guide_legend(title = NULL)) +
ylab('Reinstatement strength\n(logits)') +
xlab('Hipp quintile') +
theme(legend.title=element_blank())
p2
ggsave('~/Experiments/AP/figs/AP_reinstatement_bygroupXhippquintile_allOLD_v4.jpeg',
dpi=150, width=15, height=8)
p2
All old conditions by hipp quintile (collapse across cond)
head(dm)## # A tibble: 6 x 83
## X.x run trial onset duration shockCond shockTrial target
## <int> <dbl> <int> <dbl> <dbl> <fctr> <int> <fctr>
## 1 0 1 1 0.0191 10.6661 safe 0 BANDIT
## 2 2 1 3 19.8642 9.4224 safe 0 VIOLIN
## 3 3 1 4 29.3132 11.3389 safe 0 MEDAL
## 4 4 1 5 40.6786 9.7644 safe 0 MANURE
## 5 5 1 6 50.4701 9.1180 safe 0 SARDINE
## 6 7 1 8 69.7414 9.9338 safe 0 TOBACCO
## # ... with 75 more variables: associate <fctr>, resp <fctr>, acc <fctr>,
## # accSpec <fctr>, respRT <dbl>, subid <fctr>, remove <lgl>,
## # anxious <int>, happy <int>, safe <int>, stressed <int>,
## # life_stress <int>, sleep <dbl>, cond_orig <fctr>,
## # shock_and_post <int>, conf <fctr>, onset_adj <dbl>,
## # mem_conditions <fctr>, obj_conditions <fctr>, onset_code <fctr>,
## # rt_z <dbl>, mem_conditions_byrep <fctr>, onset_code_byrep <fctr>,
## # img_type <fctr>, X.y <int>, index <int>, group <fctr>,
## # condition <fctr>, category <fctr>, X0 <dbl>, X2 <dbl>, X4 <dbl>,
## # X6 <dbl>, X8 <dbl>, X10 <dbl>, X12 <dbl>, avg_logit <dbl>,
## # cond <fctr>, reps <fctr>, pcorr <fctr>, vtc_logit <dbl>,
## # imgType <fctr>, hipp_sig <dbl>, hipp_tail_sig <dbl>, DAN_sig <dbl>,
## # CCN_sig <dbl>, rsp_sig <dbl>, angular_lh_sig <dbl>, DAN_lh_sig <dbl>,
## # CCN_lh_sig <dbl>, ang_logit <dbl>, hipp_logit <dbl>,
## # vtc_nophc_logit <dbl>, ang_logit_z <dbl>, vtc_logit_z <dbl>,
## # hipp_logit_z <dbl>, CCN_sig_z <dbl>, rsp_sig_z <dbl>, DAN_sig_z <dbl>,
## # angular_lh_sig_z <dbl>, CCN_lh_sig_z <dbl>, DAN_lh_sig_z <dbl>,
## # hipp_sig_z <dbl>, hipp_tail_sig_z <dbl>, hipp_quintile <int>,
## # rsp_quintile <int>, CCN_quintile <int>, DAN_quintile <int>,
## # angular_lh_sig_quintile <int>, CCN_lh_quintile <int>,
## # DAN_lh_quintile <int>, hipp_tail_quintile <int>, vtc_quintile <int>,
## # hipp_logit_quintile <int>, ang_quintile <int>dm_time = dm %>%
gather('time', 'logit_vtc', `X0`:`X12`) %>%
mutate(time=gsub("X","",time)) %>%
group_by(group, subid, time, hipp_quintile) %>%
summarise(mean_logit = mean_(logit_vtc)) %>%
group_by(group, time, hipp_quintile) %>%
summarise(mean=mean_(mean_logit),
sem=std.error(mean_logit),
n=length(mean_logit)) %>%
ungroup() %>%
mutate(time = as.numeric(time))
blues <- brewer.pal(6, "Blues")[2:6]
p2 = ggplot(dm_time,
aes(x=factor(time), y=mean, group=hipp_quintile, color=factor(hipp_quintile))) +
facet_grid(.~group)+
geom_hline(yintercept = 0, linetype=2)+
geom_errorbar(width=0, aes(ymin=mean-sem,
ymax=mean+sem), size=1.5,
position=position_dodge(.5)) +
geom_line(position=position_dodge(.5), size=1.5) +
geom_point(position=position_dodge(.5), size=5) +
scale_color_manual(values = blues,
guide = guide_legend(title = NULL)) +
ylab('Reinstatement strength\n(logits)') +
xlab('Time post-stimulus onset (s)') +
theme(legend.title=element_blank())
p2
ggsave('~/Experiments/AP/figs/AP_reinstatement_bygroupXhippquintile_collapse_allOLD_ts.jpeg',
dpi=150, width=9, height=5)
p2
p2 = ggplot(dm_time,
aes(x=factor(time), y=mean, group=group, color=group)) +
facet_grid(.~hipp_quintile)+
geom_hline(yintercept = 0, linetype=2)+
geom_errorbar(width=0, aes(ymin=mean-sem,
ymax=mean+sem), size=1.5,
position=position_dodge(.5)) +
geom_line(position=position_dodge(.5), size=1.5) +
geom_point(position=position_dodge(.5), size=5) +
scale_color_manual(values = palette,
guide = guide_legend(title = NULL)) +
ylab('Reinstatement strength\n(logits)') +
xlab('Time post-stimulus onset (s)') +
theme(legend.title=element_blank())
p2
ggsave('~/Experiments/AP/figs/AP_reinstatement_bygroupXhippquintile_collapse_allOLD_ts_v2.jpeg',
dpi=150, width=15, height=5)
p2
3b) Effects of group/encoding strength on inferior parietal reinstatement
Note that these analyses are using strict criterion that VTC reinstatement F1 score can’t be an outlier (control = 22, stress = 20); see below for analyses allowing 1 participant with non-outlier inferior parietal F1 score to be included (control = 22, stress = 21).
HC assoc hits: infparietal reinstatement ~ group
subids_lowtrials = dm %>%
filter(cond == 'sourcehit') %>%
group_by(group, subid) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), fill = list(count= 0)) %>%
filter(count < 6) %>%
pull(subid) %>% unique()
subids_lowtrials## factor(0)
## 42 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ... ap173d_avg = dm %>% filter(!subid %in% subids_lowtrials) %>%
filter(cond == 'sourcehit') %>%
group_by(group, subid) %>%
summarise(mean=mean(ang_logit))
with(d_avg, table(group))## group
## control stress
## 22 20# diff between groups?
bartlett.test(mean ~ group, data=d_avg)##
## Bartlett test of homogeneity of variances
##
## data: mean by group
## Bartlett's K-squared = 0.92568, df = 1, p-value = 0.336t.test(mean ~ group, data=d_avg, var.equal=TRUE)##
## Two Sample t-test
##
## data: mean by group
## t = 0.64016, df = 40, p-value = 0.5257
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3048895 0.5875713
## sample estimates:
## mean in group control mean in group stress
## 0.3410088 0.1996679d_avg %>% group_by(group) %>% summarise(mean(mean), n())## # A tibble: 2 x 3
## group `mean(mean)` `n()`
## <fctr> <dbl> <int>
## 1 control 0.3410088 22
## 2 stress 0.1996679 20t.test(d_avg %>% filter(group == 'control') %>% pull(mean), mu = 0)##
## One Sample t-test
##
## data: d_avg %>% filter(group == "control") %>% pull(mean)
## t = 2.5122, df = 21, p-value = 0.02024
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.05871977 0.62329777
## sample estimates:
## mean of x
## 0.3410088t.test(d_avg %>% filter(group == 'stress') %>% pull(mean), mu = 0)##
## One Sample t-test
##
## data: d_avg %>% filter(group == "stress") %>% pull(mean)
## t = 1.1276, df = 19, p-value = 0.2735
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.1709514 0.5702871
## sample estimates:
## mean of x
## 0.19966794) Hipp peak from whole-brain analysis
Sphere from control>stress, SH > CR
## [,1]
## control 1
## stress -1## [,1]
## CR -1
## sourcehit 1## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ group * cond + (1 | subid)
## Data: dat
##
## REML criterion at convergence: 1241.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8742 -0.5136 0.1295 0.5402 2.3126
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 79656 282.2
## Residual 68301 261.3
## Number of obs: 88, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 219.00 50.86 42.00 4.306 9.76e-05 ***
## group1 42.52 50.86 42.00 0.836 0.4078
## cond1 199.09 27.86 43.03 7.146 7.88e-09 ***
## group1:cond1 68.31 27.86 43.03 2.452 0.0183 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 cond1
## group1 0.000
## cond1 0.000 0.000
## group1:cnd1 0.000 0.000 0.000##
## Two Sample t-test
##
## data: value by group
## t = 2.0628, df = 42, p-value = 0.04535
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 4.808099 438.516200
## sample estimates:
## mean in group control mean in group stress
## 528.9209 307.2588##
## Two Sample t-test
##
## data: value by group
## t = -0.41617, df = 42, p-value = 0.6794
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -301.6367 198.4988
## sample estimates:
## mean in group control mean in group stress
## -5.884154 45.684789## # A tibble: 4 x 5
## group cond value sem n
## <fctr> <fctr> <dbl> <dbl> <int>
## 1 control CR -5.884154 89.14745 22
## 2 control HC
## assoc hit 528.920947 70.92192 22
## 3 stress CR 45.684789 86.06576 22
## 4 stress HC
## assoc hit 307.258798 80.72654 22
## # A tibble: 88 x 4
## subid group cond mean
## <fctr> <fctr> <fctr> <dbl>
## 1 ap100 control CR -395.94870
## 2 ap100 control HC
## assoc hit 379.93707
## 3 ap101 control CR 319.92365
## 4 ap101 control HC
## assoc hit 990.58691
## 5 ap102 control CR 107.35379
## 6 ap102 control HC
## assoc hit 891.54816
## 7 ap103 control CR -519.10510
## 8 ap103 control HC
## assoc hit 90.65915
## 9 ap104 control CR -415.79956
## 10 ap104 control HC
## assoc hit -99.19929
## # ... with 78 more rows
## Warning: Ignoring unknown aesthetics: y
5) Predictors of HC accuracy
Calculate P(accurate|respond “high conf source” to old item)
with(dtest %>% filter(reps > 0,
conf == 'Hi'), table(subj_status, acc))## acc
## subj_status CR FA H M no response SM
## old 0 0 3025 0 0 706dtest %>%
filter(subj_status == 'old',
reps > 0, # old item
conf == 'Hi') %>% dim()## [1] 3731 15dtest %>%
filter(subj_status == 'old',
reps > 0, # old item
conf == 'Hi',
acc_num == 1) %>% dim() # 3025## [1] 3025 15dat = dtest %>%
filter(subj_status == 'old',
reps > 0, # old item
conf == 'Hi') %>%
group_by(subid, group) %>%
summarise(mean_acc = mean(acc_num), sum=sum(acc_num), count=n()) %>%
ungroup() %>%
complete(nesting(subid, group), fill = list(mean_acc=0,
count=0))
dat## # A tibble: 47 x 5
## subid group mean_acc sum count
## <fctr> <fctr> <dbl> <dbl> <dbl>
## 1 ap100 control 0.8725490 89 102
## 2 ap101 control 0.9375000 105 112
## 3 ap102 control 0.7010309 68 97
## 4 ap103 control 0.8968254 113 126
## 5 ap104 control 0.9130435 147 161
## 6 ap105 control 0.8623188 119 138
## 7 ap106 control 0.5087719 29 57
## 8 ap107 control 0.8802817 125 142
## 9 ap108 control 0.7073171 29 41
## 10 ap109 control 0.6489362 61 94
## # ... with 37 more rows# filter out subjs who don't have more than 5 "old" responses to old cues
dim(dat) # all subjs included## [1] 47 5dat %>% filter(count < 6) ## # A tibble: 1 x 5
## subid group mean_acc sum count
## <fctr> <fctr> <dbl> <dbl> <dbl>
## 1 ap151 stress 0.6 3 5rm_subids = dat %>% filter(count < 6) %>% pull(subid) %>% unique(); rm_subids## [1] ap151
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174rm_subids # ap151## [1] ap151
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174dat = dat %>% filter(!subid %in% rm_subids)
# logit transform accuracy for stats (since upper bounded at 1)
dat %>% group_by(subid, group) %>%
summarise(mean_acc = mean(mean_acc), n()) %>%
group_by(group) %>%
summarise(mean(mean_acc), n())## # A tibble: 2 x 3
## group `mean(mean_acc)` `n()`
## <fctr> <dbl> <int>
## 1 control 0.8185684 23
## 2 stress 0.7387830 23hist(dat$mean_acc)
dat = dat %>% mutate(mean_acc_logit = car::logit(mean_acc))
hist(dat$mean_acc_logit)
boxplot(dat$mean_acc_logit)
Across all subjs (regardless of scan-criteria) effect of group on acc?
bartlett.test(mean_acc_logit ~ group, data=dat)##
## Bartlett test of homogeneity of variances
##
## data: mean_acc_logit by group
## Bartlett's K-squared = 0.14041, df = 1, p-value = 0.7079t.test(mean_acc_logit ~ group, data=dat, var.equal=TRUE)##
## Two Sample t-test
##
## data: mean_acc_logit by group
## t = 1.9585, df = 44, p-value = 0.05653
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01457236 1.01850230
## sample estimates:
## mean in group control mean in group stress
## 1.706974 1.205009dat_control = dat %>% filter(group == 'control')
t.test(x=dat_control$mean_acc_logit, mu = 0)##
## One Sample t-test
##
## data: dat_control$mean_acc_logit
## t = 9.8233, df = 22, p-value = 1.668e-09
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 1.346600 2.067347
## sample estimates:
## mean of x
## 1.706974dat_control = dat %>% filter(group == 'stress')
t.test(x=dat_control$mean_acc_logit, mu = 0)##
## One Sample t-test
##
## data: dat_control$mean_acc_logit
## t = 6.3961, df = 22, p-value = 1.953e-06
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.8142949 1.5957230
## sample estimates:
## mean of x
## 1.205009Is group -> source accuracy mediated by hippocampal activity for SHs (>CRs)?
Load in hippocampal ROI data
roi = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/pe_sourcehit-cr_zstat1_peak1_5mm_sphere_masked.nii.csv')
roi = roi %>% filter(cond %in% c('sourcehit', 'CR')) %>%
mutate(cond = factor(cond)) %>%
dplyr::select(subid, group, cond, value) %>% # calc diff score
spread(key = cond, value=value) %>%
mutate(diff = sourcehit - CR) %>%
left_join(dat, by = c('subid', 'group')) %>%
mutate(diff_orig = diff,
diff = as.numeric(scale(diff)),
mean = mean_acc,
mean_acc_logit = as.numeric(car::logit(mean_acc)),
mean_acc = as.numeric(scale(car::logit(mean_acc))))## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vectorroi$subid= factor(roi$subid)
str(roi)## 'data.frame': 44 obs. of 11 variables:
## $ subid : Factor w/ 44 levels "ap100","ap101",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## $ CR : num -396 320 107 -519 -416 ...
## $ sourcehit : num 379.9 990.6 891.5 90.7 -99.2 ...
## $ diff : num 0.967 0.698 0.988 0.542 -0.209 ...
## $ mean_acc : num 0.47 1.429 -0.84 0.762 0.993 ...
## $ sum : num 89 105 68 113 147 119 125 29 61 20 ...
## $ count : num 102 112 97 126 161 138 142 41 94 21 ...
## $ mean_acc_logit: num 1.924 2.708 0.852 2.162 2.351 ...
## $ diff_orig : num 776 671 784 610 317 ...
## $ mean : num 0.873 0.938 0.701 0.897 0.913 ...contrasts(roi$group) = c(1,-1)
with(roi, hist(mean_acc))
with(roi, hist(mean))
roi %>% group_by(group) %>% summarise(mean(mean), mean(mean_acc), n())## # A tibble: 2 x 4
## group `mean(mean)` `mean(mean_acc)` `n()`
## <fctr> <dbl> <dbl> <int>
## 1 control 0.8326500 0.2978779 22
## 2 stress 0.7581595 -0.2978779 22Mediation of group -> assoc acc by posterior hipp BOLD
model.t = lm(mean_acc ~ group, data=roi); summary(model.t) # total effect = 0.30##
## Call:
## lm(formula = mean_acc ~ group, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.72781 -0.73339 0.06102 0.56616 1.73891
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.072e-16 1.455e-01 0.000 1.0000
## group1 2.979e-01 1.455e-01 2.048 0.0468 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9648 on 42 degrees of freedom
## Multiple R-squared: 0.09079, Adjusted R-squared: 0.06915
## F-statistic: 4.194 on 1 and 42 DF, p-value: 0.04685# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 1.072e-16 1.455e-01 0.000 1.0000
# group1 2.979e-01 1.455e-01 2.048 0.0468 *
model.m = lm(diff ~ group, data=roi); summary(model.m) # a = 0.35 ##
## Call:
## lm(formula = diff ~ group, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.84011 -0.56394 0.07552 0.61793 2.94691
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.289e-16 1.427e-01 0.000 1.0000
## group1 3.498e-01 1.427e-01 2.452 0.0185 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9464 on 42 degrees of freedom
## Multiple R-squared: 0.1252, Adjusted R-squared: 0.1044
## F-statistic: 6.012 on 1 and 42 DF, p-value: 0.01845# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -1.289e-16 1.427e-01 0.000 1.0000
# group1 3.498e-01 1.427e-01 2.452 0.0185 *
model.y = lm(mean_acc ~ group + diff, data=roi); summary(model.y)##
## Call:
## lm(formula = mean_acc ~ group + diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0503 -0.5369 0.0118 0.4692 1.7126
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.694e-16 1.297e-01 0.000 1.00000
## group1 1.290e-01 1.386e-01 0.931 0.35745
## diff 4.827e-01 1.402e-01 3.442 0.00134 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8601 on 41 degrees of freedom
## Multiple R-squared: 0.2946, Adjusted R-squared: 0.2602
## F-statistic: 8.562 on 2 and 41 DF, p-value: 0.0007814# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 1.694e-16 1.297e-01 0.000 1.00000
# group1 1.290e-01 1.386e-01 0.931 0.35745
# diff 4.827e-01 1.402e-01 3.442 0.00134 **
model.other = lm(mean_acc ~ diff, data=roi); summary(model.other)##
## Call:
## lm(formula = mean_acc ~ diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.19405 -0.44648 -0.01872 0.56127 1.82297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.791e-16 1.295e-01 0.000 1.000000
## diff 5.289e-01 1.310e-01 4.038 0.000224 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8587 on 42 degrees of freedom
## Multiple R-squared: 0.2797, Adjusted R-squared: 0.2626
## F-statistic: 16.31 on 1 and 42 DF, p-value: 0.0002237# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 1.791e-16 1.295e-01 0.000 1.000000
# diff 5.289e-01 1.310e-01 4.038 0.000224 ***
# Multiple R-squared: 0.2797, Adjusted R-squared: 0.2626
str(roi)## 'data.frame': 44 obs. of 11 variables:
## $ subid : Factor w/ 44 levels "ap100","ap101",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] 1 -1
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr "control" "stress"
## .. .. ..$ : NULL
## $ CR : num -396 320 107 -519 -416 ...
## $ sourcehit : num 379.9 990.6 891.5 90.7 -99.2 ...
## $ diff : num 0.967 0.698 0.988 0.542 -0.209 ...
## $ mean_acc : num 0.47 1.429 -0.84 0.762 0.993 ...
## $ sum : num 89 105 68 113 147 119 125 29 61 20 ...
## $ count : num 102 112 97 126 161 138 142 41 94 21 ...
## $ mean_acc_logit: num 1.924 2.708 0.852 2.162 2.351 ...
## $ diff_orig : num 776 671 784 610 317 ...
## $ mean : num 0.873 0.938 0.701 0.897 0.913 ...with(roi, plot(mean_acc ~ diff))
plot(model.other)
# does this hold for each group?
with(roi %>% filter(group == 'control'), plot(mean_acc ~ diff))
with(roi %>% filter(group == 'stress'), plot(mean_acc ~ diff))
summary(lm(mean_acc ~ diff, data=roi %>% filter(group == 'control')))##
## Call:
## lm(formula = mean_acc ~ diff, data = roi %>% filter(group ==
## "control"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.36817 -0.62660 0.07546 0.37310 1.71925
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1717 0.2053 0.836 0.413
## diff 0.3608 0.2118 1.703 0.104
##
## Residual standard error: 0.898 on 20 degrees of freedom
## Multiple R-squared: 0.1266, Adjusted R-squared: 0.08298
## F-statistic: 2.9 on 1 and 20 DF, p-value: 0.1041summary(lm(mean_acc ~ diff, data=roi %>% filter(group == 'stress')))##
## Call:
## lm(formula = mean_acc ~ diff, data = roi %>% filter(group ==
## "stress"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.12482 -0.32794 -0.06431 0.65673 1.16147
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.09003 0.18811 -0.479 0.63743
## diff 0.59418 0.18668 3.183 0.00467 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8275 on 20 degrees of freedom
## Multiple R-squared: 0.3362, Adjusted R-squared: 0.303
## F-statistic: 10.13 on 1 and 20 DF, p-value: 0.004675# robust regression since outliers/small sample sizes
library(MASS)
roi %>% filter(group == 'control') %>% summarise(n())## n()
## 1 22summary(rlm(mean_acc ~ diff, data=roi %>% filter(group == 'control')))##
## Call: rlm(formula = mean_acc ~ diff, data = roi %>% filter(group ==
## "control"))
## Residuals:
## Min 1Q Median 3Q Max
## -1.36876 -0.59275 0.08731 0.38659 1.73679
##
## Coefficients:
## Value Std. Error t value
## (Intercept) 0.1416 0.2583 0.5481
## diff 0.3918 0.2666 1.4697
##
## Residual standard error: 0.8403 on 20 degrees of freedompt(q = 1.4697, df = 20, lower.tail = FALSE)## [1] 0.07859963summary(rlm(mean_acc ~ diff, data=roi %>% filter(group == 'stress')))##
## Call: rlm(formula = mean_acc ~ diff, data = roi %>% filter(group ==
## "stress"))
## Residuals:
## Min 1Q Median 3Q Max
## -2.18918 -0.38212 -0.08375 0.59762 1.11121
##
## Coefficients:
## Value Std. Error t value
## (Intercept) -0.0398 0.2122 -0.1876
## diff 0.6386 0.2105 3.0331
##
## Residual standard error: 0.63 on 20 degrees of freedompt(q = 3.0331, df = 20, lower.tail = FALSE)## [1] 0.003283615# does variability in these measures vary across groups?
roi %>% filter(group == 'control') %>% pull(mean_acc) %>% sd()## [1] 0.9377136roi %>% filter(group == 'stress') %>% pull(mean_acc) %>% sd()## [1] 0.9911605roi %>% filter(group == 'control') %>% pull(diff) %>% sd()## [1] 0.9250079roi %>% filter(group == 'stress') %>% pull(diff) %>% sd()## [1] 0.9672594## Bootstrapping Confidence Interval (BCa and Percentile)
boot.med <- function(data, indices){
data <-data[indices,] #this does the random resampling of rows
reg1 <- lm(diff ~ group, data=data)
reg2 <- lm(mean_acc ~ group + diff, data=data)
a <- coefficients(reg1)["group1"] #the a path coefficient
b <- coefficients(reg2)["diff"] #the b path coefficient
a*b #returns the product of a*b
}
medboot<-boot(data=roi, statistic = boot.med, R=5000)
mean(medboot$t)## [1] 0.1645124quantile(medboot$t, c(0.025, 0.975))## 2.5% 97.5%
## 0.02385291 0.34857500# RESULTS:
# > mean(medboot$t)
# [1] 0.1645428
# > quantile(medboot$t, c(0.025, 0.975))
# 2.5% 97.5%
# 0.0254956 0.3545451 Plot
p = ggplot(roi, aes(x=diff_orig, y=mean_acc_logit, color=group)) +
geom_point() +
geom_smooth(method=lm, color='black') +
geom_point(aes(color=group), size=3, alpha=.8) +
scale_color_manual(values=c('dodgerblue', 'orange')) +
xlab('Hippocampal BOLD (a.u.)') +
ylab('HC associative accuracy\n(logit transformed)') +
theme(legend.title=element_blank(), legend.position = c(0.2, 0.9),
plot.margin = unit(c(2, 2, 1, 1), "lines"))
ggsave('~/Experiments/AP/figs/AP_hippXacc.jpeg', dpi=150, width=5.5, height=4.3)
ggsave('~/Dropbox/Stanford/Papers/Dissertation/Figures_Defense/AP_hippXacc.png', dpi=300, width=5.5, height=4.3)
p
Check that this still holds when removing 2 subs w/bad VTC:
subids_rm = c('ap168', 'ap174')
roi = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/pe_sourcehit-cr_zstat1_peak1_5mm_sphere_masked.nii.csv')
roi = roi %>% filter(!subid %in% subids_rm,
cond %in% c('sourcehit', 'CR')) %>%
mutate(cond = factor(cond)) %>%
dplyr::select(subid, group, cond, value) %>% # calc diff score
spread(key = cond, value=value) %>%
mutate(diff = sourcehit - CR) %>%
left_join(dat, by = c('subid', 'group')) %>%
mutate(diff_orig = diff,
diff = as.numeric(scale(diff)),
mean = mean_acc,
mean_acc = as.numeric(scale(car::logit(mean_acc))))## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vectorroi$subid= factor(roi$subid)
str(roi)## 'data.frame': 42 obs. of 11 variables:
## $ subid : Factor w/ 42 levels "ap100","ap101",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## $ CR : num -396 320 107 -519 -416 ...
## $ sourcehit : num 379.9 990.6 891.5 90.7 -99.2 ...
## $ diff : num 0.924 0.652 0.945 0.495 -0.263 ...
## $ mean_acc : num 0.451 1.433 -0.892 0.75 0.987 ...
## $ sum : num 89 105 68 113 147 119 125 29 61 20 ...
## $ count : num 102 112 97 126 161 138 142 41 94 21 ...
## $ mean_acc_logit: num 1.924 2.708 0.852 2.162 2.351 ...
## $ diff_orig : num 776 671 784 610 317 ...
## $ mean : num 0.873 0.938 0.701 0.897 0.913 ...contrasts(roi$group) = c(1,-1)
roi %>% group_by(group) %>% summarise(mean(mean), mean(mean_acc), n())## # A tibble: 2 x 4
## group `mean(mean)` `mean(mean_acc)` `n()`
## <fctr> <dbl> <dbl> <int>
## 1 control 0.8326500 0.2744294 22
## 2 stress 0.7647046 -0.3018723 20model.t = lm(mean_acc ~ group,
data=roi); summary(model.t) ##
## Call:
## lm(formula = mean_acc ~ group, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.80490 -0.75033 0.04542 0.52653 1.78204
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01372 0.14961 -0.092 0.9274
## group1 0.28815 0.14961 1.926 0.0612 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9685 on 40 degrees of freedom
## Multiple R-squared: 0.08486, Adjusted R-squared: 0.06198
## F-statistic: 3.709 on 1 and 40 DF, p-value: 0.06124# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -0.01372 0.14961 -0.092 0.9274
# group1 0.28815 0.14961 1.926 0.0612 .
model.m = lm(diff ~ group,
data=roi); summary(model.m) ##
## Call:
## lm(formula = diff ~ group, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8664 -0.5615 0.1202 0.5771 2.9000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01504 0.14821 -0.102 0.9197
## group1 0.31594 0.14821 2.132 0.0392 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9594 on 40 degrees of freedom
## Multiple R-squared: 0.102, Adjusted R-squared: 0.07957
## F-statistic: 4.544 on 1 and 40 DF, p-value: 0.03922# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -0.01504 0.14821 -0.102 0.9197
# group1 0.31594 0.14821 2.132 0.0392 *
model.y = lm(mean_acc ~ group + diff,
data=roi); summary(model.y)##
## Call:
## lm(formula = mean_acc ~ group + diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.08607 -0.49224 0.01576 0.38171 1.75627
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.006673 0.134229 -0.050 0.96061
## group1 0.140123 0.141631 0.989 0.32859
## diff 0.468537 0.143185 3.272 0.00224 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8688 on 39 degrees of freedom
## Multiple R-squared: 0.282, Adjusted R-squared: 0.2452
## F-statistic: 7.659 on 2 and 39 DF, p-value: 0.001565# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -0.006673 0.134229 -0.050 0.96061
# group1 0.140123 0.141631 0.989 0.32859
# diff 0.468537 0.143185 3.272 0.00224 **
model.other = lm(mean_acc ~ diff,
data=roi); summary(model.other)##
## Call:
## lm(formula = mean_acc ~ diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.24505 -0.45864 -0.00737 0.52330 1.87362
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.593e-17 1.340e-01 0.000 1.000000
## diff 5.138e-01 1.356e-01 3.788 0.000501 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8686 on 40 degrees of freedom
## Multiple R-squared: 0.264, Adjusted R-squared: 0.2456
## F-statistic: 14.35 on 1 and 40 DF, p-value: 0.0005012# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -2.593e-17 1.340e-01 0.000 1.000000
# diff 5.138e-01 1.356e-01 3.788 0.000501 ***
# Multiple R-squared: 0.264, Adjusted R-squared: 0.2456
str(roi)## 'data.frame': 42 obs. of 11 variables:
## $ subid : Factor w/ 42 levels "ap100","ap101",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] 1 -1
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr "control" "stress"
## .. .. ..$ : NULL
## $ CR : num -396 320 107 -519 -416 ...
## $ sourcehit : num 379.9 990.6 891.5 90.7 -99.2 ...
## $ diff : num 0.924 0.652 0.945 0.495 -0.263 ...
## $ mean_acc : num 0.451 1.433 -0.892 0.75 0.987 ...
## $ sum : num 89 105 68 113 147 119 125 29 61 20 ...
## $ count : num 102 112 97 126 161 138 142 41 94 21 ...
## $ mean_acc_logit: num 1.924 2.708 0.852 2.162 2.351 ...
## $ diff_orig : num 776 671 784 610 317 ...
## $ mean : num 0.873 0.938 0.701 0.897 0.913 ...with(roi, plot(mean_acc ~ diff))
# does this hold for each group?
with(roi %>% filter(group == 'control'), plot(mean_acc ~ diff))
with(roi %>% filter(group == 'stress',
!subid %in% subids_rm), plot(mean_acc ~ diff))
summary(lm(mean_acc ~ diff, data=roi %>% filter(group == 'control')))##
## Call:
## lm(formula = mean_acc ~ diff, data = roi %>% filter(group ==
## "control"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.40211 -0.64214 0.07733 0.38236 1.76189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1642 0.2066 0.795 0.436
## diff 0.3663 0.2151 1.703 0.104
##
## Residual standard error: 0.9202 on 20 degrees of freedom
## Multiple R-squared: 0.1266, Adjusted R-squared: 0.08298
## F-statistic: 2.9 on 1 and 20 DF, p-value: 0.1041summary(lm(mean_acc ~ diff, data=roi %>% filter(group == 'stress')))##
## Call:
## lm(formula = mean_acc ~ diff, data = roi %>% filter(group ==
## "stress"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.14672 -0.31308 -0.01764 0.47936 1.21700
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1133 0.1940 -0.584 0.56638
## diff 0.5696 0.1907 2.987 0.00791 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8206 on 18 degrees of freedom
## Multiple R-squared: 0.3314, Adjusted R-squared: 0.2942
## F-statistic: 8.92 on 1 and 18 DF, p-value: 0.007912# robust regression since outliers/small sample sizes
library(MASS)
summary(rlm(mean_acc ~ diff, data=roi %>% filter(group == 'control')))##
## Call: rlm(formula = mean_acc ~ diff, data = roi %>% filter(group ==
## "control"))
## Residuals:
## Min 1Q Median 3Q Max
## -1.40271 -0.60746 0.08947 0.39618 1.77987
##
## Coefficients:
## Value Std. Error t value
## (Intercept) 0.1350 0.2600 0.5194
## diff 0.3978 0.2707 1.4697
##
## Residual standard error: 0.8611 on 20 degrees of freedompt(q = 1.4697, df = 20, lower.tail = FALSE)## [1] 0.07859963summary(rlm(mean_acc ~ diff, data=roi %>% filter(group == 'stress')))##
## Call: rlm(formula = mean_acc ~ diff, data = roi %>% filter(group ==
## "stress"))
## Residuals:
## Min 1Q Median 3Q Max
## -2.20935 -0.35669 -0.08597 0.46470 1.15984
##
## Coefficients:
## Value Std. Error t value
## (Intercept) -0.0629 0.1898 -0.3315
## diff 0.6148 0.1865 3.2967
##
## Residual standard error: 0.5525 on 18 degrees of freedompt(q = 3.2967, df = 20, lower.tail = FALSE)## [1] 0.001801785## Bootstrapping Confidence Interval (BCa and Percentile)
boot.med <- function(data, indices){
data <-data[indices,] #this does the random resampling of rows
reg1 <- lm(diff ~ group, data=data)
reg2 <- lm(mean_acc ~ group + diff, data=data)
a <- coefficients(reg1)["group1"] #the a path coefficient
b <- coefficients(reg2)["diff"] #the b path coefficient
a*b #returns the product of a*b
}
medboot<-boot(data=roi,
statistic = boot.med, R=5000)
mean(medboot$t)## [1] 0.1407147quantile(medboot$t, c(0.025, 0.975))## 2.5% 97.5%
## 0.00384396 0.30884270# RESULTS:
# > mean(medboot$t)
# [1] 0.1415879
# > quantile(medboot$t, c(0.025, 0.975))
# 2.5% 97.5%
# -0.0001349303 0.3141738951Is hipp effect on source accuracy mediated by VTC reinstatement?
vtc = read.csv('/Volumes/group/awagner/sgagnon/AP/results/sourcehit_avglogit_vtc.csv')
vtc = vtc %>% mutate(mean_logit = mean) %>% dplyr::select(-mean)
roi = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/pe_sourcehit-cr_zstat1_peak1_5mm_sphere_masked.nii.csv')
roi = roi %>% filter(cond %in% c('sourcehit', 'CR')) %>%
mutate(cond = factor(cond)) %>%
dplyr::select(subid, group, cond, value) %>% # calc diff score
spread(key = cond, value=value) %>%
mutate(diff = sourcehit - CR) %>%
right_join(vtc, by = c('subid', 'group')) %>%
left_join(dat, by = c('subid', 'group')) %>%
mutate(diff_orig = diff,
diff = as.numeric(scale(diff)),
logit_orig = mean_logit,
mean_logit = scale(mean_logit),
mean_acc_logit = as.numeric(car::logit(mean_acc)),
mean_acc = as.numeric(scale(car::logit(mean_acc))))## Warning in right_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vector## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vectordim(roi)## [1] 42 13str(roi)## 'data.frame': 42 obs. of 13 variables:
## $ subid : chr "ap100" "ap101" "ap102" "ap103" ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## $ CR : num -396 320 107 -519 -416 ...
## $ sourcehit : num 379.9 990.6 891.5 90.7 -99.2 ...
## $ diff : num 0.924 0.652 0.945 0.495 -0.263 ...
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ mean_logit : num [1:42, 1] 0.3483 -0.2448 0.7178 -0.8012 -0.0116 ...
## ..- attr(*, "scaled:center")= num 0.854
## ..- attr(*, "scaled:scale")= num 0.66
## $ mean_acc : num 0.451 1.433 -0.892 0.75 0.987 ...
## $ sum : num 89 105 68 113 147 119 125 29 61 20 ...
## $ count : num 102 112 97 126 161 138 142 41 94 21 ...
## $ mean_acc_logit: num 1.924 2.708 0.852 2.162 2.351 ...
## $ diff_orig : num 776 671 784 610 317 ...
## $ logit_orig : num 1.084 0.693 1.328 0.326 0.847 ...roi %>% group_by(group) %>% summarise(n()) # n=22, 20## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 22
## 2 stress 20contrasts(roi$group) = c(1,-1)
model.t = lm(mean_acc ~ diff, data=roi); summary(model.t) ##
## Call:
## lm(formula = mean_acc ~ diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.24505 -0.45864 -0.00737 0.52330 1.87362
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.593e-17 1.340e-01 0.000 1.000000
## diff 5.138e-01 1.356e-01 3.788 0.000501 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8686 on 40 degrees of freedom
## Multiple R-squared: 0.264, Adjusted R-squared: 0.2456
## F-statistic: 14.35 on 1 and 40 DF, p-value: 0.0005012# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -2.593e-17 1.340e-01 0.000 1.000000
# diff 5.138e-01 1.356e-01 3.788 0.000501 ***
model.m = lm(mean_logit ~ diff, data=roi); summary(model.m)##
## Call:
## lm(formula = mean_logit ~ diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.27791 -0.36144 0.08478 0.46973 1.73971
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.251e-17 1.336e-01 0.000 1.000000
## diff 5.187e-01 1.352e-01 3.837 0.000433 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8656 on 40 degrees of freedom
## Multiple R-squared: 0.2691, Adjusted R-squared: 0.2508
## F-statistic: 14.72 on 1 and 40 DF, p-value: 0.0004327# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 4.251e-17 1.336e-01 0.000 1.000000
# diff 5.187e-01 1.352e-01 3.837 0.000433 ***
model.y = lm(mean_acc ~ mean_logit + diff, data=roi); summary(model.y)##
## Call:
## lm(formula = mean_acc ~ mean_logit + diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.23819 -0.47077 0.00205 0.53071 1.87638
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.756e-17 1.356e-01 0.000 1.00000
## mean_logit 3.837e-02 1.606e-01 0.239 0.81240
## diff 4.939e-01 1.606e-01 3.076 0.00383 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.879 on 39 degrees of freedom
## Multiple R-squared: 0.265, Adjusted R-squared: 0.2274
## F-statistic: 7.032 on 2 and 39 DF, p-value: 0.002466# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -2.756e-17 1.356e-01 0.000 1.00000
# mean_logit 3.837e-02 1.606e-01 0.239 0.81240
# diff 4.939e-01 1.606e-01 3.076 0.00383 **
model.other = lm(mean_acc ~ mean_logit, data=roi); summary(model.other) #marginal##
## Call:
## lm(formula = mean_acc ~ mean_logit, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0953 -0.7960 -0.1424 0.7132 2.0233
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.227e-17 1.493e-01 0.000 1.0000
## mean_logit 2.946e-01 1.511e-01 1.949 0.0583 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9675 on 40 degrees of freedom
## Multiple R-squared: 0.08676, Adjusted R-squared: 0.06393
## F-statistic: 3.8 on 1 and 40 DF, p-value: 0.05829# (Intercept) -3.227e-17 1.493e-01 0.000 1.0000
# mean_logit 2.946e-01 1.511e-01 1.949 0.0583 .
# Multiple R-squared: 0.08676, Adjusted R-squared: 0.06393
# any effect of group on reinstatement, when controlling for hipp?
summary(lm(mean_logit ~ diff + group, data=roi))##
## Call:
## lm(formula = mean_logit ~ diff + group, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.26845 -0.36500 0.07507 0.45777 1.73456
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0005613 0.1354209 -0.004 0.996714
## diff 0.5149071 0.1444561 3.564 0.000982 ***
## group1 0.0117880 0.1428881 0.082 0.934673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8765 on 39 degrees of freedom
## Multiple R-squared: 0.2692, Adjusted R-squared: 0.2317
## F-statistic: 7.183 on 2 and 39 DF, p-value: 0.002209## Bootstrapping Confidence Interval (BCa and Percentile)
boot.med <- function(data, indices){
data <-data[indices,] #this does the random resampling of rows
reg1 <- lm(mean_logit ~ diff, data=data)
reg2 <- lm(mean_acc ~ mean_logit + diff, data=data)
a <- coefficients(reg1)["diff"] #the a path coefficient
b <- coefficients(reg2)["mean_logit"] #the b path coefficient
a*b #returns the product of a*b
}
medboot<-boot(data=roi, statistic = boot.med, R=5000)
mean(medboot$t)## [1] 0.021213quantile(medboot$t, c(0.025, 0.975))## 2.5% 97.5%
## -0.1464613 0.1902283# RESULTS:
# > mean(medboot$t)
# [1] 0.0239775
# > quantile(medboot$t, c(0.025, 0.975))
# 2.5% 97.5%
# -0.1401416 0.1945644 Plots
ggplot(roi, aes(x=logit_orig, y=mean_acc_logit, color=group)) +
geom_point() +
geom_smooth(method=lm, color='black') +
geom_point(aes(color=group), size=3, alpha=.8) +
scale_color_manual(values=c('dodgerblue', 'orange')) +
xlab('Reinstatement strength (logits)') +
ylab('HC associative accuracy\n(logit transformed)') +
theme(legend.title=element_blank(), legend.position = c(0.2, 0.9),
plot.margin = unit(c(2, 2, 1, 1), "lines"))
ggsave('~/Experiments/AP/figs/AP_logitXacc.jpeg', dpi=150, width=5.5, height=4.3)
ggsave('~/Dropbox/Stanford/Papers/Dissertation/Figures_Defense/AP_logitXacc.png', dpi=300, width=5.5, height=4.3)
ggplot(roi, aes(x=diff, y=logit_orig, color=group)) +
geom_point() +
geom_smooth(method=lm, color='black') +
geom_point(aes(color=group), size=3, alpha=.8) +
scale_color_manual(values=c('dodgerblue', 'orange')) +
xlab('Hippocampal BOLD (a.u.)') +
ylab('Reinstatement strength (logits)') +
theme(legend.title=element_blank(), legend.position = c(0.2, 0.9),
plot.margin = unit(c(2, 2, 1, 1), "lines"))
ggsave('~/Experiments/AP/figs/AP_logitXhipp.jpeg', dpi=150, width=5.5, height=4.3)
ggsave('~/Dropbox/Stanford/Papers/Dissertation/Figures_Defense/AP_logitXhipp.png', dpi=300, width=5.5, height=4.8)roi = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/pe_sourcehit-cr_zstat1_peak1_5mm_sphere_masked.nii.csv')
roi = roi %>% filter(cond %in% c('sourcehit', 'CR')) %>%
mutate(cond = factor(cond)) %>%
dplyr::select(subid, group, cond, value) %>% # calc diff score
spread(key = cond, value=value) %>%
mutate(diff = sourcehit - CR) %>%
right_join(slopes_ccn_rt, by = c('subid')) %>%
left_join(dat, by = c('subid', 'group')) %>%
mutate(diff_orig = diff,
diff = as.numeric(scale(diff)),
slope_orig = coef,
scaled_coef = as.numeric(scale(coef)),
mean_acc = as.numeric(scale(car::logit(mean_acc))))## Warning in right_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vectordim(roi)## [1] 42 13str(roi)## 'data.frame': 42 obs. of 13 variables:
## $ subid : chr "ap100" "ap101" "ap102" "ap103" ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## $ CR : num -396 320 107 -519 -416 ...
## $ sourcehit : num 379.9 990.6 891.5 90.7 -99.2 ...
## $ diff : num 0.924 0.652 0.945 0.495 -0.263 ...
## $ coef : num 0.185 0.205 0.146 0.164 0.175 ...
## $ mean_acc : num 0.451 1.433 -0.892 0.75 0.987 ...
## $ sum : num 89 105 68 113 147 119 125 29 61 20 ...
## $ count : num 102 112 97 126 161 138 142 41 94 21 ...
## $ mean_acc_logit: num 1.924 2.708 0.852 2.162 2.351 ...
## $ diff_orig : num 776 671 784 610 317 ...
## $ slope_orig : num 0.185 0.205 0.146 0.164 0.175 ...
## $ scaled_coef : num 0.365 0.488 0.132 0.241 0.308 ...contrasts(roi$group) = c(1,-1)
summary(lm(scaled_coef ~ group, data=roi))##
## Call:
## lm(formula = scaled_coef ~ group, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.58283 -0.30618 -0.07339 0.46506 2.56691
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01883 0.14336 -0.131 0.89615
## group1 0.39545 0.14336 2.759 0.00871 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.928 on 40 degrees of freedom
## Multiple R-squared: 0.1598, Adjusted R-squared: 0.1388
## F-statistic: 7.61 on 1 and 40 DF, p-value: 0.008712summary(lm(mean_acc ~ scaled_coef + group, data=roi))##
## Call:
## lm(formula = mean_acc ~ scaled_coef + group, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.47825 -0.64423 -0.07226 0.65890 1.80703
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.007718 0.144308 -0.053 0.9576
## scaled_coef 0.318828 0.159131 2.004 0.0521 .
## group1 0.162070 0.157403 1.030 0.3095
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.934 on 39 degrees of freedom
## Multiple R-squared: 0.1703, Adjusted R-squared: 0.1277
## F-statistic: 4.002 on 2 and 39 DF, p-value: 0.02626summary(lm(mean_acc ~ scaled_coef + group + diff, data=roi))##
## Call:
## lm(formula = mean_acc ~ scaled_coef + group + diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.77166 -0.48848 -0.08121 0.42874 1.78043
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.001192 0.128654 -0.009 0.99265
## scaled_coef 0.300171 0.141963 2.114 0.04110 *
## group1 0.025041 0.146227 0.171 0.86494
## diff 0.457074 0.137317 3.329 0.00195 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8326 on 38 degrees of freedom
## Multiple R-squared: 0.3576, Adjusted R-squared: 0.3069
## F-statistic: 7.05 on 3 and 38 DF, p-value: 0.0006981Is hipp effect on source accuracy mediated by infparietal reinstatement?
Note that this analysis is excluding 2 subjs w/poor VTC localizer classification.
vtc = read.csv('/Volumes/group/awagner/sgagnon/AP/results/sourcehit_avglogit_infparietal.csv')
vtc = vtc %>% mutate(mean_logit = mean) %>% dplyr::select(-mean)
dim(vtc)## [1] 42 4vtc %>% group_by(subid, group) %>% summarise(n()) %>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 22
## 2 stress 20roi = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/pe_sourcehit-cr_zstat1_peak1_5mm_sphere_masked.nii.csv')
roi = roi %>% filter(cond %in% c('sourcehit', 'CR')) %>%
mutate(cond = factor(cond)) %>%
dplyr::select(subid, group, cond, value) %>% # calc diff score
spread(key = cond, value=value) %>%
mutate(diff = sourcehit - CR) %>%
right_join(vtc, by = c('subid', 'group')) %>%
left_join(dat, by = c('subid', 'group')) %>%
mutate(diff_orig = diff,
diff = scale(diff),
mean_logit = scale(mean_logit),
mean_acc = as.numeric(scale(car::logit(mean_acc))))## Warning in right_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vector## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vectordim(roi)## [1] 42 12str(roi)## 'data.frame': 42 obs. of 12 variables:
## $ subid : chr "ap100" "ap101" "ap102" "ap103" ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## $ CR : num -396 320 107 -519 -416 ...
## $ sourcehit : num 379.9 990.6 891.5 90.7 -99.2 ...
## $ diff : num [1:42, 1] 0.924 0.652 0.945 0.495 -0.263 ...
## ..- attr(*, "scaled:center")= num 418
## ..- attr(*, "scaled:scale")= num 387
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ mean_logit : num [1:42, 1] -0.0671 0.2441 0.8726 -1.4194 -0.1556 ...
## ..- attr(*, "scaled:center")= num 0.274
## ..- attr(*, "scaled:scale")= num 0.709
## $ mean_acc : num 0.451 1.433 -0.892 0.75 0.987 ...
## $ sum : num 89 105 68 113 147 119 125 29 61 20 ...
## $ count : num 102 112 97 126 161 138 142 41 94 21 ...
## $ mean_acc_logit: num 1.924 2.708 0.852 2.162 2.351 ...
## $ diff_orig : num 776 671 784 610 317 ...contrasts(roi$group) = c(1,-1)
model.t = lm(mean_acc ~ diff, data=roi); summary(model.t) ##
## Call:
## lm(formula = mean_acc ~ diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.24505 -0.45864 -0.00737 0.52330 1.87362
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.593e-17 1.340e-01 0.000 1.000000
## diff 5.138e-01 1.356e-01 3.788 0.000501 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8686 on 40 degrees of freedom
## Multiple R-squared: 0.264, Adjusted R-squared: 0.2456
## F-statistic: 14.35 on 1 and 40 DF, p-value: 0.0005012model.m = lm(mean_logit ~ diff, data=roi); summary(model.m)##
## Call:
## lm(formula = mean_logit ~ diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5604 -0.6458 -0.1284 0.5507 2.2856
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.630e-17 1.497e-01 0.00 1.0000
## diff 2.849e-01 1.516e-01 1.88 0.0674 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9705 on 40 degrees of freedom
## Multiple R-squared: 0.08119, Adjusted R-squared: 0.05821
## F-statistic: 3.534 on 1 and 40 DF, p-value: 0.06741model.y = lm(mean_acc ~ mean_logit + diff, data=roi); summary(model.y)##
## Call:
## lm(formula = mean_acc ~ mean_logit + diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.23418 -0.46397 -0.00129 0.51094 1.86800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.431e-17 1.354e-01 0.000 1.00000
## mean_logit 6.171e-02 1.430e-01 0.432 0.66842
## diff 4.962e-01 1.430e-01 3.471 0.00128 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8775 on 39 degrees of freedom
## Multiple R-squared: 0.2675, Adjusted R-squared: 0.2299
## F-statistic: 7.12 on 2 and 39 DF, p-value: 0.002312model.other = lm(mean_acc ~ mean_logit, data=roi); summary(model.other)##
## Call:
## lm(formula = mean_acc ~ mean_logit, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0866 -0.7677 -0.1106 0.7970 2.0174
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.278e-17 1.530e-01 0.000 1.000
## mean_logit 2.031e-01 1.548e-01 1.312 0.197
##
## Residual standard error: 0.9913 on 40 degrees of freedom
## Multiple R-squared: 0.04125, Adjusted R-squared: 0.01728
## F-statistic: 1.721 on 1 and 40 DF, p-value: 0.1971## Bootstrapping Confidence Interval (BCa and Percentile)
boot.med <- function(data, indices){
data <-data[indices,] #this does the random resampling of rows
reg1 <- lm(mean_logit ~ diff, data=data)
reg2 <- lm(mean_acc ~ mean_logit + diff, data=data)
a <- coefficients(reg1)["diff"] #the a path coefficient
b <- coefficients(reg2)["mean_logit"] #the b path coefficient
a*b #returns the product of a*b
}
medboot<-boot(data=roi, statistic = boot.med, R=5000)
mean(medboot$t)## [1] 0.01262749quantile(medboot$t, c(0.025, 0.975))## 2.5% 97.5%
## -0.08775011 0.105276205b) Predictors of LC accuracy
dat = dtest %>%
filter(subj_status == 'old',
reps > 0,
conf == 'Lo') %>%
group_by(subid, group) %>%
summarise(mean_acc = mean(acc_num), count=n()) %>%
ungroup() %>%
complete(nesting(subid, group), fill = list(mean_acc=0,
count=0))
dim(dat) # missing 104, with no LC responses## [1] 46 4dat %>% filter(count < 6) ## # A tibble: 3 x 4
## subid group mean_acc count
## <fctr> <fctr> <dbl> <dbl>
## 1 ap116 control 0.6666667 3
## 2 ap160 stress 1.0000000 1
## 3 ap169 stress 0.0000000 2rm_subids = dat %>% filter(count < 6) %>% pull(subid) %>% unique()
rm_subids## [1] ap116 ap160 ap169
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174dat = dat %>% filter(!subid %in% rm_subids)
dat %>% group_by(subid, group) %>% summarise(n()) %>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 21
## 2 stress 22hist(dat$mean_acc)
dat = dat%>% mutate(mean_acc_logit = car::logit(mean_acc))
hist(dat$mean_acc_logit)
bartlett.test(mean_acc_logit ~ group, data=dat)##
## Bartlett test of homogeneity of variances
##
## data: mean_acc_logit by group
## Bartlett's K-squared = 3.1647, df = 1, p-value = 0.07525t.test(mean_acc_logit ~ group, data=dat, var.equal=TRUE)##
## Two Sample t-test
##
## data: mean_acc_logit by group
## t = 2.5569, df = 41, p-value = 0.01436
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.04654901 0.39641939
## sample estimates:
## mean in group control mean in group stress
## 0.28273408 0.06124988dat_control = dat %>% filter(group == 'control')
t.test(x=dat_control$mean_acc_logit, mu = 0)##
## One Sample t-test
##
## data: dat_control$mean_acc_logit
## t = 3.8644, df = 20, p-value = 0.0009659
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.1301165 0.4353517
## sample estimates:
## mean of x
## 0.2827341dat_control = dat %>% filter(group == 'stress')
t.test(x=dat_control$mean_acc_logit, mu = 0)##
## One Sample t-test
##
## data: dat_control$mean_acc_logit
## t = 1.2805, df = 21, p-value = 0.2143
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.03822232 0.16072209
## sample estimates:
## mean of x
## 0.06124988dat## # A tibble: 43 x 5
## subid group mean_acc count mean_acc_logit
## <fctr> <fctr> <dbl> <dbl> <dbl>
## 1 ap100 control 0.4400000 25 -0.24116206
## 2 ap101 control 0.6666667 21 0.69314718
## 3 ap102 control 0.4285714 63 -0.28768207
## 4 ap103 control 0.5909091 22 0.36772478
## 5 ap105 control 0.7058824 17 0.87546874
## 6 ap106 control 0.6400000 75 0.57536414
## 7 ap107 control 0.6470588 17 0.60613580
## 8 ap108 control 0.6086957 69 0.44183275
## 9 ap109 control 0.5000000 46 0.00000000
## 10 ap110 control 0.5140187 107 0.05608947
## # ... with 33 more rowsDoes hipp track LC accuracy?
roi = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/pe_sourcehit-cr_zstat1_peak1_5mm_sphere_masked.nii.csv')
roi %>% group_by(subid, group) %>% summarise(n()) %>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 22
## 2 stress 22roi = roi %>% filter(cond %in% c('sourcehit', 'CR')) %>%
mutate(cond = factor(cond)) %>%
dplyr::select(subid, group, cond, value) %>% # calc diff score
spread(key = cond, value=value) %>%
mutate(diff = sourcehit - CR) %>%
inner_join(dat, by = c('subid', 'group')) %>%
mutate(diff_orig = diff,
diff = scale(diff),
mean_acc = as.numeric(scale(car::logit(mean_acc))))## Warning in inner_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vectordim(roi)## [1] 40 9str(roi)## 'data.frame': 40 obs. of 9 variables:
## $ subid : chr "ap100" "ap101" "ap102" "ap103" ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## $ CR : num -396 320 107 -519 706 ...
## $ sourcehit : num 379.9 990.6 891.5 90.7 1278.5 ...
## $ diff : num [1:40, 1] 0.916 0.654 0.937 0.502 0.409 ...
## ..- attr(*, "scaled:center")= num 408
## ..- attr(*, "scaled:scale")= num 401
## $ mean_acc : num -1.309 1.747 -1.461 0.682 2.343 ...
## $ count : num 25 21 63 22 17 17 69 46 107 68 ...
## $ mean_acc_logit: num -0.241 0.693 -0.288 0.368 0.875 ...
## $ diff_orig : num 776 671 784 610 573 ...roi %>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 20
## 2 stress 20contrasts(roi$group) = c(1,-1)
model.t = lm(mean_acc ~ diff, data=roi); summary(model.t) ##
## Call:
## lm(formula = mean_acc ~ diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.81880 -0.58962 0.03591 0.57668 2.23727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.070e-18 1.547e-01 0.000 1.000
## diff 2.583e-01 1.567e-01 1.648 0.108
##
## Residual standard error: 0.9787 on 38 degrees of freedom
## Multiple R-squared: 0.06674, Adjusted R-squared: 0.04218
## F-statistic: 2.717 on 1 and 38 DF, p-value: 0.1075ggplot(roi, aes(x=diff_orig, y=mean_acc)) +
geom_point() +
geom_smooth(method=lm) +
xlab('Hippocampus SH > CR') +
ylab('Low conf source accuracy (logit)')
6) Effects of stress on frontoparietal networks (and interactions by run type)
basedir = '/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/'
# dt = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/mvpa/notebooks/ap_behav.csv') #already read in
# head(dt)
counts = dt %>%
filter(mem_conditions %in% c('CR', 'sourcehit')) %>%
group_by(subid, mem_conditions) %>%
summarise(n=n()) %>%
ungroup() %>%
complete(subid, mem_conditions, fill = list(n=0))
# filter(mem_conditions %in% c('sourcehit', 'CR'))
# length(unique(dt$mem_conditions)) * length(unique(dt$subid))
sub_remove = counts %>%
filter(mem_conditions %in% c('sourcehit', 'CR'),
n <= 5) %>%
pull(subid); sub_remove## [1] ap151 ap156
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174sub_remove## [1] ap151 ap156
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174dt %>%
filter(mem_conditions %in% c('CR', 'sourcehit')) %>%
mutate(mem_conditions = factor(mem_conditions)) %>%
group_by(subid, shockCond, mem_conditions) %>%
summarise(n=n()) %>%
ungroup() %>%
complete(subid, shockCond, mem_conditions, fill = list(n=0)) %>%
filter(n < 6)## # A tibble: 8 x 4
## subid shockCond mem_conditions n
## <fctr> <fctr> <fctr> <dbl>
## 1 ap121 threat CR 1
## 2 ap151 safe sourcehit 2
## 3 ap151 threat sourcehit 1
## 4 ap156 safe sourcehit 0
## 5 ap156 threat sourcehit 5
## 6 ap158 safe sourcehit 5
## 7 ap164 safe CR 5
## 8 ap173 threat sourcehit 1dt %>%
filter(mem_conditions %in% c('CR', 'sourcehit')) %>%
mutate(mem_conditions = factor(mem_conditions)) %>%
group_by(subid, group, shockCond, mem_conditions) %>%
summarise(n=n()) %>%
ungroup() %>%
complete(nesting(subid, group),
shockCond, mem_conditions, fill = list(n=0)) %>%
group_by(subid, group) %>% summarise(min = min(n)) %>%
filter(min > 5) %>%
group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control-fmri 21
## 2 stress-fmri 20# and ap106 has too much motion, so n = 20 control6a) CCN
d2 = read.csv(paste(basedir,'pe_frontoparietal.csv', sep=''))
data = d2 %>%
filter(cond %in% c('sourcehit', 'CR')) %>%
mutate(cond = factor(cond)) %>%
filter(!(subid %in% sub_remove))
data %>% group_by(subid, group) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 22
## 2 stress 22contrasts(data$group) = c(1, -1); contrasts(data$group)## [,1]
## control 1
## stress -1data$cond = factor(data$cond)
contrasts(data$cond) = c(-1,1); contrasts(data$cond)## [,1]
## CR -1
## sourcehit 1contrasts(data$hemi) = c(1,-1); contrasts(data$hemi)## [,1]
## lh 1
## rh -1summary(lmer(value ~ group * cond + (1|subid),
data=data %>% filter(hemi == 'lh')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ group * cond + (1 | subid)
## Data: data %>% filter(hemi == "lh")
##
## REML criterion at convergence: 1328.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.67637 -0.56419 -0.06267 0.41560 2.39691
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 187800 433.4
## Residual 210164 458.4
## Number of obs: 88, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 242.70 81.59 42.00 2.975 0.00484 **
## group1 -14.20 81.59 42.00 -0.174 0.86268
## cond1 599.74 48.87 40.43 12.272 3.11e-15 ***
## group1:cond1 76.59 48.87 40.43 1.567 0.12488
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 cond1
## group1 0.000
## cond1 0.000 0.000
## group1:cnd1 0.000 0.000 0.000summary(lmer(value ~ group * cond * hemi + (1 + cond + hemi|subid), data=data)) # including cond RE not enough obs## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ group * cond * hemi + (1 + cond + hemi | subid)
## Data: data
##
## REML criterion at convergence: 2577.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.09901 -0.45367 0.03082 0.45005 2.04655
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 218481 467.4
## cond1 53497 231.3 -0.21
## hemi1 11592 107.7 0.23 0.89
## Residual 79029 281.1
## Number of obs: 176, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.012 73.583 42.000 -0.068 0.946
## group1 -10.210 73.583 42.000 -0.139 0.890
## cond1 382.772 40.803 42.000 9.381 7.29e-12 ***
## hemi1 247.711 26.692 42.000 9.280 9.94e-12 ***
## group1:cond1 54.942 40.803 42.000 1.347 0.185
## group1:hemi1 -3.988 26.692 42.000 -0.149 0.882
## cond1:hemi1 216.966 21.190 41.110 10.239 7.08e-13 ***
## group1:cond1:hemi1 21.643 21.190 41.110 1.021 0.313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 cond1 hemi1 grp1:c1 grp1:h1 cnd1:1
## group1 0.000
## cond1 -0.168 0.000
## hemi1 0.135 0.000 0.462
## group1:cnd1 0.000 -0.168 0.000 0.000
## group1:hem1 0.000 0.135 0.000 0.000 0.462
## cond1:hemi1 0.000 0.000 0.000 0.000 0.000 0.000
## grp1:cnd1:1 0.000 0.000 0.000 0.000 0.000 0.000 0.0006b) DAN
d2 = read.csv(paste(basedir,'pe_dorsalattn.csv', sep=''))
data = d2 %>%
filter(cond %in% c('sourcehit', 'CR')) %>%
mutate(cond = factor(cond)) %>%
filter(!(subid %in% sub_remove))
data %>% group_by(subid, group) %>% summarise(n()) %>%
group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 22
## 2 stress 22contrasts(data$group) = c(1, -1); contrasts(data$group)## [,1]
## control 1
## stress -1data$cond = factor(data$cond)
contrasts(data$cond) = c(-1,1); contrasts(data$cond)## [,1]
## CR -1
## sourcehit 1contrasts(data$hemi) = c(1,-1); contrasts(data$hemi)## [,1]
## lh 1
## rh -1summary(lmer(value ~ group * cond + (1|subid),
data=data %>% filter(hemi == 'lh')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ group * cond + (1 | subid)
## Data: data %>% filter(hemi == "lh")
##
## REML criterion at convergence: 1340.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3926 -0.4691 0.0128 0.5090 2.2320
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 346686 588.8
## Residual 185004 430.1
## Number of obs: 88, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1040.58 99.91 42.00 10.415 3.28e-13 ***
## group1 176.51 99.91 42.00 1.767 0.084543 .
## cond1 194.05 45.85 41.08 4.232 0.000127 ***
## group1:cond1 59.92 45.85 41.08 1.307 0.198526
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 cond1
## group1 0.000
## cond1 0.000 0.000
## group1:cnd1 0.000 0.000 0.000summary(lmer(value ~ group * cond * hemi + (1 + cond + hemi|subid), data=data)) # including cond RE not enough obs## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ group * cond * hemi + (1 + cond + hemi | subid)
## Data: data
##
## REML criterion at convergence: 2551.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.43574 -0.37814 0.00211 0.43352 1.32768
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 330476 574.9
## cond1 73038 270.3 0.04
## hemi1 32412 180.0 0.32 0.15
## Residual 18949 137.7
## Number of obs: 176, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 790.81 87.28 42.00 9.060 1.96e-11 ***
## group1 121.07 87.28 42.00 1.387 0.17274
## cond1 132.16 42.04 42.00 3.143 0.00306 **
## hemi1 249.76 29.06 42.00 8.596 8.39e-11 ***
## group1:cond1 45.04 42.04 42.00 1.071 0.29014
## group1:hemi1 55.44 29.06 42.00 1.908 0.06324 .
## cond1:hemi1 61.89 10.38 42.03 5.965 4.45e-07 ***
## group1:cond1:hemi1 14.88 10.38 42.03 1.434 0.15896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 cond1 hemi1 grp1:c1 grp1:h1 cnd1:1
## group1 0.000
## cond1 0.035 0.000
## hemi1 0.299 0.000 0.133
## group1:cnd1 0.000 0.035 0.000 0.000
## group1:hem1 0.000 0.299 0.000 0.000 0.133
## cond1:hemi1 0.000 0.000 0.000 0.000 0.000 0.000
## grp1:cnd1:1 0.000 0.000 0.000 0.000 0.000 0.000 0.0006c) Effect of shock condition – CCN
basedir = '/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw-byshockCond/group/roi/'
d2 = read.csv(paste(basedir,'pe_frontoparietal.csv', sep=''))
glimpse(d2)## Observations: 960
## Variables: 10
## $ X <int> 12, 13, 42, 43, 72, 73, 102, 103, 132, 133, 162, 163...
## $ cond <fctr> CR_safe, CR_safe, FA_safe, FA_safe, sourcehit_safe,...
## $ hemi <fctr> lh, rh, lh, rh, lh, rh, lh, rh, lh, rh, lh, rh, lh,...
## $ mask_vox <dbl> 1530, 1802, 1530, 1802, 1530, 1802, 1530, 1802, 1530...
## $ regspace <fctr> epi, epi, epi, epi, epi, epi, epi, epi, epi, epi, e...
## $ roi <fctr> frontoparietal, frontoparietal, frontoparietal, fro...
## $ smoothing <fctr> unsmoothed, unsmoothed, unsmoothed, unsmoothed, uns...
## $ subid <fctr> ap100, ap100, ap100, ap100, ap100, ap100, ap100, ap...
## $ value <dbl> -742.15948, -1334.28943, 2084.60303, 362.16690, 1286...
## $ group <fctr> control, control, control, control, control, contro...data = d2 %>%
separate(cond, into = c('this', 'shockCond'), sep = ".*(_)", remove = FALSE) %>%
separate(cond, into = c('cond', 'that'), sep = "_[^_]*$") %>%
dplyr::select(-one_of('this', 'that')) %>%
filter(cond %in% c('sourcehit', 'CR')) %>%
mutate(cond=factor(cond),
shockCond=factor(shockCond))
glimpse(data)## Observations: 320
## Variables: 11
## $ X <int> 12, 13, 72, 73, 192, 193, 252, 253, 372, 373, 432, 4...
## $ cond <fctr> CR, CR, sourcehit, sourcehit, CR, CR, sourcehit, so...
## $ shockCond <fctr> safe, safe, safe, safe, threat, threat, threat, thr...
## $ hemi <fctr> lh, rh, lh, rh, lh, rh, lh, rh, lh, rh, lh, rh, lh,...
## $ mask_vox <dbl> 1530, 1802, 1530, 1802, 1530, 1802, 1530, 1802, 1366...
## $ regspace <fctr> epi, epi, epi, epi, epi, epi, epi, epi, epi, epi, e...
## $ roi <fctr> frontoparietal, frontoparietal, frontoparietal, fro...
## $ smoothing <fctr> unsmoothed, unsmoothed, unsmoothed, unsmoothed, uns...
## $ subid <fctr> ap100, ap100, ap100, ap100, ap100, ap100, ap100, ap...
## $ value <dbl> -742.159485, -1334.289429, 1286.854736, -810.972168,...
## $ group <fctr> control, control, control, control, control, contro...data %>% group_by(subid, shockCond, group) %>%
summarise(n()) %>%
group_by(shockCond, group) %>% summarise(n())## Source: local data frame [4 x 3]
## Groups: shockCond [?]
##
## # A tibble: 4 x 3
## shockCond group `n()`
## <fctr> <fctr> <int>
## 1 safe control 20
## 2 safe stress 20
## 3 threat control 20
## 4 threat stress 20contrasts(data$group) = c(1, -1); contrasts(data$group)## [,1]
## control 1
## stress -1contrasts(data$cond) =c(-1,1); contrasts(data$cond)## [,1]
## CR -1
## sourcehit 1contrasts(data$hemi) =c(1,-1); contrasts(data$hemi)## [,1]
## lh 1
## rh -1contrasts(data$shockCond) =c(1,-1); contrasts(data$shockCond)## [,1]
## safe 1
## threat -1# interaction within stress group?
dim(data %>% filter(group == 'stress', hemi == 'lh'))## [1] 80 11summary(lmer(value ~ cond*shockCond + (1+cond+shockCond|subid),
data=data %>% filter(group == 'stress', hemi == 'lh'))) #interaction n.obs## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ cond * shockCond + (1 + cond + shockCond | subid)
## Data: data %>% filter(group == "stress", hemi == "lh")
##
## REML criterion at convergence: 1220.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.83558 -0.49259 0.03846 0.42857 2.41266
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 324198 569.4
## cond1 72894 270.0 0.42
## shockCond1 19768 140.6 -0.48 -1.00
## Residual 216015 464.8
## Number of obs: 80, groups: subid, 20
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 313.78 137.51 19.00 2.282 0.0342 *
## cond1 505.28 79.65 19.42 6.343 3.94e-06 ***
## shockCond1 -63.36 60.73 24.44 -1.043 0.3070
## cond1:shockCond1 -60.28 51.96 38.13 -1.160 0.2532
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cond1 shckC1
## cond1 0.297
## shockCond1 -0.229 -0.392
## cnd1:shckC1 0.000 0.000 0.000# (Intercept) 313.78 137.51 19.00 2.282 0.0342 *
# cond1 505.28 79.65 19.42 6.343 3.94e-06 ***
# shockCond1 -63.36 60.73 24.44 -1.043 0.3070
# cond1:shockCond1 -60.28 51.96 38.13 -1.160 0.2532
summary(lmer(value ~ cond*shockCond*hemi + (1+cond*shockCond+ hemi|subid),
data=data %>% filter(group == 'stress')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ cond * shockCond * hemi + (1 + cond * shockCond + hemi |
## subid)
## Data: data %>% filter(group == "stress")
##
## REML criterion at convergence: 2348.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.23273 -0.47086 -0.07589 0.44680 2.00733
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 252403 502.4
## cond1 70791 266.1 0.04
## shockCond1 48186 219.5 -0.08 -0.51
## hemi1 27961 167.2 0.48 0.65 -0.19
## cond1:shockCond1 48884 221.1 0.30 -0.05 -0.23 0.32
## Residual 66380 257.6
## Number of obs: 160, groups: subid, 20
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 64.7658 114.1711 19.0000 0.567 0.577172
## cond1 302.0229 62.8841 19.0000 4.803 0.000124 ***
## shockCond1 -54.4050 53.1431 19.0000 -1.024 0.318814
## hemi1 249.0117 42.5785 19.0000 5.848 1.24e-05 ***
## cond1:shockCond1 -59.4611 53.4702 19.0000 -1.112 0.279994
## cond1:hemi1 203.2599 20.3685 57.4400 9.979 3.73e-14 ***
## shockCond1:hemi1 -8.9561 20.3685 57.4400 -0.440 0.661802
## cond1:shockCond1:hemi1 -0.8226 20.3685 57.4400 -0.040 0.967926
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cond1 shckC1 hemi1 cn1:C1 cnd1:1 shC1:1
## cond1 0.040
## shockCond1 -0.077 -0.444
## hemi1 0.418 0.543 -0.155
## cnd1:shckC1 0.277 -0.046 -0.196 0.257
## cond1:hemi1 0.000 0.000 0.000 0.000 0.000
## shckCnd1:h1 0.000 0.000 0.000 0.000 0.000 0.000
## cnd1:shC1:1 0.000 0.000 0.000 0.000 0.000 0.000 0.0006d) Effect of shock condition – DAN
basedir = '/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw-byshockCond/group/roi/'
d2 = read.csv(paste(basedir,'pe_dorsalattn.csv', sep=''))
glimpse(d2)## Observations: 960
## Variables: 10
## $ X <int> 14, 15, 44, 45, 74, 75, 104, 105, 134, 135, 164, 165...
## $ cond <fctr> CR_safe, CR_safe, FA_safe, FA_safe, sourcehit_safe,...
## $ hemi <fctr> lh, rh, lh, rh, lh, rh, lh, rh, lh, rh, lh, rh, lh,...
## $ mask_vox <dbl> 726, 695, 726, 695, 726, 695, 726, 695, 726, 695, 72...
## $ regspace <fctr> epi, epi, epi, epi, epi, epi, epi, epi, epi, epi, e...
## $ roi <fctr> dorsalattn, dorsalattn, dorsalattn, dorsalattn, dor...
## $ smoothing <fctr> unsmoothed, unsmoothed, unsmoothed, unsmoothed, uns...
## $ subid <fctr> ap100, ap100, ap100, ap100, ap100, ap100, ap100, ap...
## $ value <dbl> 94.16875, -685.06036, 3071.22925, 2113.89307, 830.55...
## $ group <fctr> control, control, control, control, control, contro...data = d2 %>%
separate(cond, into = c('this', 'shockCond'), sep = ".*(_)", remove = FALSE) %>%
separate(cond, into = c('cond', 'that'), sep = "_[^_]*$") %>%
dplyr::select(-one_of('this', 'that')) %>%
filter(cond %in% c('sourcehit', 'CR')) %>%
mutate(cond=factor(cond),
shockCond=factor(shockCond))
glimpse(data)## Observations: 320
## Variables: 11
## $ X <int> 14, 15, 74, 75, 194, 195, 254, 255, 374, 375, 434, 4...
## $ cond <fctr> CR, CR, sourcehit, sourcehit, CR, CR, sourcehit, so...
## $ shockCond <fctr> safe, safe, safe, safe, threat, threat, threat, thr...
## $ hemi <fctr> lh, rh, lh, rh, lh, rh, lh, rh, lh, rh, lh, rh, lh,...
## $ mask_vox <dbl> 726, 695, 726, 695, 726, 695, 726, 695, 720, 557, 72...
## $ regspace <fctr> epi, epi, epi, epi, epi, epi, epi, epi, epi, epi, e...
## $ roi <fctr> dorsalattn, dorsalattn, dorsalattn, dorsalattn, dor...
## $ smoothing <fctr> unsmoothed, unsmoothed, unsmoothed, unsmoothed, uns...
## $ subid <fctr> ap100, ap100, ap100, ap100, ap100, ap100, ap100, ap...
## $ value <dbl> 94.16875, -685.06036, 830.55835, -296.97443, 31.3087...
## $ group <fctr> control, control, control, control, control, contro...data %>% group_by(subid, shockCond, group) %>%
summarise(n()) %>%
group_by(shockCond, group) %>% summarise(n())## Source: local data frame [4 x 3]
## Groups: shockCond [?]
##
## # A tibble: 4 x 3
## shockCond group `n()`
## <fctr> <fctr> <int>
## 1 safe control 20
## 2 safe stress 20
## 3 threat control 20
## 4 threat stress 20contrasts(data$group) = c(1, -1); contrasts(data$group)## [,1]
## control 1
## stress -1contrasts(data$cond) =c(-1,1); contrasts(data$cond)## [,1]
## CR -1
## sourcehit 1contrasts(data$hemi) =c(1,-1); contrasts(data$hemi)## [,1]
## lh 1
## rh -1contrasts(data$shockCond) =c(1,-1); contrasts(data$shockCond)## [,1]
## safe 1
## threat -1# interaction within stress group?
dim(data %>% filter(group == 'stress', hemi == 'lh'))## [1] 80 11summary(lmer(value ~ cond*shockCond + (1+cond+shockCond|subid),
data=data %>% filter(group == 'stress', hemi == 'lh'))) #interaction n.obs## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ cond * shockCond + (1 + cond + shockCond | subid)
## Data: data %>% filter(group == "stress", hemi == "lh")
##
## REML criterion at convergence: 1213.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.89728 -0.40508 0.08397 0.42095 2.50886
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 417423 646.1
## cond1 19424 139.4 0.27
## shockCond1 50476 224.7 -0.48 0.27
## Residual 174796 418.1
## Number of obs: 80, groups: subid, 20
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 960.80 151.84 19.00 6.328 4.51e-06 ***
## cond1 115.33 56.18 19.00 2.053 0.0541 .
## shockCond1 -104.28 68.62 19.00 -1.520 0.1451
## cond1:shockCond1 -95.52 46.74 20.50 -2.044 0.0541 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cond1 shckC1
## cond1 0.140
## shockCond1 -0.335 0.110
## cnd1:shckC1 0.000 0.000 0.000summary(lmer(value ~ cond*shockCond*hemi + (1+cond*shockCond+ hemi|subid),
data=data %>% filter(group == 'stress')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ cond * shockCond * hemi + (1 + cond * shockCond + hemi |
## subid)
## Data: data %>% filter(group == "stress")
##
## REML criterion at convergence: 2266
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.24562 -0.43528 -0.07142 0.45734 2.39872
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 326781 571.6
## cond1 58642 242.2 0.13
## shockCond1 77988 279.3 -0.26 0.08
## hemi1 20711 143.9 0.66 -0.26 -0.62
## cond1:shockCond1 43061 207.5 0.55 -0.11 -0.44 0.49
## Residual 20981 144.8
## Number of obs: 160, groups: subid, 20
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 756.860 128.336 19.000 5.897 1.12e-05 ***
## cond1 65.897 55.347 19.000 1.191 0.2485
## shockCond1 -97.803 63.486 19.000 -1.541 0.1399
## hemi1 203.939 34.157 19.000 5.971 9.56e-06 ***
## cond1:shockCond1 -93.279 47.793 19.000 -1.952 0.0659 .
## cond1:hemi1 49.438 11.451 56.700 4.317 6.41e-05 ***
## shockCond1:hemi1 -6.476 11.451 56.700 -0.566 0.5740
## cond1:shockCond1:hemi1 -2.244 11.451 56.700 -0.196 0.8453
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cond1 shckC1 hemi1 cn1:C1 cnd1:1 shC1:1
## cond1 0.122
## shockCond1 -0.252 0.081
## hemi1 0.618 -0.243 -0.575
## cnd1:shckC1 0.536 -0.100 -0.424 0.450
## cond1:hemi1 0.000 0.000 0.000 0.000 0.000
## shckCnd1:h1 0.000 0.000 0.000 0.000 0.000 0.000
## cnd1:shC1:1 0.000 0.000 0.000 0.000 0.000 0.000 0.000# look at interaction
summary(lmer(value ~ cond + (1|subid),
data=data %>% filter(group == 'stress', hemi == 'lh', shockCond == 'safe'))) ## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ cond + (1 | subid)
## Data: data %>% filter(group == "stress", hemi == "lh", shockCond ==
## "safe")
##
## REML criterion at convergence: 606.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.87847 -0.43256 0.06895 0.45898 1.80406
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 312946 559.4
## Residual 205107 452.9
## Number of obs: 40, groups: subid, 20
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 856.52 144.14 19.00 5.942 1.01e-05 ***
## cond1 19.81 71.61 18.89 0.277 0.785
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## cond1 0.000summary(lmer(value ~ cond + (1|subid),
data=data %>% filter(group == 'stress', hemi == 'lh', shockCond == 'threat'))) ## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ cond + (1 | subid)
## Data: data %>% filter(group == "stress", hemi == "lh", shockCond ==
## "threat")
##
## REML criterion at convergence: 617.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.76777 -0.48790 0.04159 0.33508 1.54798
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 584002 764.2
## Residual 222181 471.4
## Number of obs: 40, groups: subid, 20
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1065.08 186.43 19.00 5.713 1.66e-05 ***
## cond1 210.86 74.53 19.52 2.829 0.0105 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## cond1 0.000# interaction by group?
summary(lmer(value ~ cond*shockCond*group + (1+cond+shockCond|subid),
data=data %>% filter(hemi == 'lh')))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ cond * shockCond * group + (1 + cond + shockCond | subid)
## Data: data %>% filter(hemi == "lh")
##
## REML criterion at convergence: 2411.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.93914 -0.38178 0.03728 0.35496 2.43019
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 377584 614.5
## cond1 33559 183.2 0.34
## shockCond1 40566 201.4 -0.20 0.08
## Residual 128068 357.9
## Number of obs: 160, groups: subid, 40
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1067.28 101.19 38.00 10.547 7.61e-13 ***
## cond1 201.96 40.49 38.00 4.988 1.38e-05 ***
## shockCond1 -45.27 42.60 38.00 -1.063 0.2946
## group1 106.48 101.19 38.00 1.052 0.2993
## cond1:shockCond1 -33.07 28.29 35.96 -1.169 0.2501
## cond1:group1 86.62 40.49 38.00 2.139 0.0389 *
## shockCond1:group1 59.01 42.60 38.00 1.385 0.1740
## cond1:shockCond1:group1 62.45 28.29 35.96 2.207 0.0338 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cond1 shckC1 group1 cn1:C1 cnd1:1 shC1:1
## cond1 0.233
## shockCond1 -0.145 0.042
## group1 0.000 0.000 0.000
## cnd1:shckC1 0.000 0.000 0.000 0.000
## cond1:grop1 0.000 0.000 0.000 0.233 0.000
## shckCnd1:g1 0.000 0.000 0.000 -0.145 0.000 0.042
## cnd1:shC1:1 0.000 0.000 0.000 0.000 0.000 0.000 0.000dcomb = data %>%
filter(hemi == 'lh') %>%
group_by(group, subid, cond, shockCond) %>%
summarise(mean = mean_(value), n()) %>%
ungroup() %>%
mutate(cond = as.character(cond),
cond = revalue(cond, c("sourcehit" = "HC\nassoc hit")),
cond = factor(cond, levels=c('HC\nassoc hit', 'CR')),
shockCond = revalue(shockCond, c("safe" = "Safe")),
shockCond = revalue(shockCond, c("threat" = "Threat")))
dcomb## # A tibble: 160 x 6
## group subid cond shockCond mean `n()`
## <fctr> <fctr> <fctr> <fctr> <dbl> <int>
## 1 control ap100 CR Safe 94.16875 1
## 2 control ap100 CR Threat 31.30874 1
## 3 control ap100 HC
## assoc hit Safe 830.55835 1
## 4 control ap100 HC
## assoc hit Threat 627.75049 1
## 5 control ap101 CR Safe 1225.35022 1
## 6 control ap101 CR Threat 899.82275 1
## 7 control ap101 HC
## assoc hit Safe 1859.50208 1
## 8 control ap101 HC
## assoc hit Threat 1769.30457 1
## 9 control ap102 CR Safe 455.47006 1
## 10 control ap102 CR Threat 1004.34052 1
## # ... with 150 more rowsdplot = data %>%
filter(hemi == 'lh') %>%
group_by(group, subid, cond, shockCond) %>%
summarise(mean_pe = mean_(value)) %>%
group_by(group, cond, shockCond) %>%
summarise(mean=mean_(mean_pe), sem=std.error(mean_pe), n=length(mean_pe)) %>%
ungroup() %>%
mutate(cond = as.character(cond),
cond = revalue(cond, c("sourcehit" = "HC\nassoc hit")),
cond = factor(cond, levels=c('HC\nassoc hit', 'CR')),
shockCond = revalue(shockCond, c("safe" = "Safe")),
shockCond = revalue(shockCond, c("threat" = "Threat")))
dplot## # A tibble: 8 x 6
## group cond shockCond mean sem n
## <fctr> <fctr> <fctr> <dbl> <dbl> <int>
## 1 control CR Safe 869.5471 139.6489 20
## 2 control CR Threat 900.8179 127.0413 20
## 3 control HC
## assoc hit Safe 1505.4680 185.9139 20
## 4 control HC
## assoc hit Threat 1419.2165 171.1199 20
## 5 stress CR Safe 836.7073 134.0977 20
## 6 stress CR Threat 854.2198 223.6807 20
## 7 stress HC
## assoc hit Safe 876.3318 183.9108 20
## 8 stress HC
## assoc hit Threat 1275.9342 174.8865 20p2 = ggplot(dplot, aes(x=cond, y=mean, group=group, fill=group)) +
facet_grid(.~shockCond) +
geom_bar(position = position_dodge(width=0.9), stat="identity") +
geom_errorbar(width=0, aes(ymin=mean-sem,
ymax=mean+sem), size=1.5, position=position_dodge(width=0.9)) +
scale_fill_manual(values=palette, guide = guide_legend(title = NULL)) +
ylab('Parameter estimate (a.u.)') +
xlab('')
p2
ggsave('~/Experiments/AP/figs/AP_DAN_byruntype.jpeg', dpi=300, width=8, height=4)
p2
## plot individual subjs
p = ggplot(dcomb,
aes(x=cond, y=mean, color=group)) +
facet_grid(.~shockCond) +
geom_point(pch = 19, position = position_jitterdodge(),
alpha=.2, size=1.5)+
scale_x_discrete(name = "Encoding strength") +
geom_point(data = dplot, aes(y=mean), position=position_dodge(.9),
alpha=1, size = 2.5, shape=19) +
geom_errorbar(data = dplot, width=0,
aes(y=mean, ymin=mean-sem, ymax=mean+sem),
size=1.5, position=position_dodge(.9), alpha=1) +
scale_color_manual(values=palette) +
ylab('Parameter estimate (a.u.)') +
xlab('')+
theme_classic(base_size = 14)+
theme(legend.title=element_blank(),
strip.text = element_text(size=14),
strip.background = element_rect(colour="white", fill="white"))## Warning: Ignoring unknown aesthetics: yp
ggsave('~/Dropbox/Stanford/Papers/AP/Figures/AP_supp_figure5.tiff', dpi=600, width=6.69, height=3)7) Variability in neural patterns during retrieval
Does the control group have more variable responses across VTC when making SHs? Evidence for “categorical” responses in stress group?
d_2dsd = read.csv('/Volumes/group/awagner/sgagnon/AP/results/retrieval_2dsd_SH_bilat-parahipp_fusi_inftemp_nohipp.csv')
str(d_2dsd)## 'data.frame': 44 obs. of 5 variables:
## $ X : int 0 1 2 3 4 5 6 7 8 9 ...
## $ subid : Factor w/ 44 levels "ap100","ap101",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ sh_count: num 88 102 66 113 146 119 124 28 61 20 ...
## $ sh_2dsd : num 45.1 45.3 48.4 42.5 44.9 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...d_2dsd %>% group_by(group) %>% summarise(n(), mean(sh_2dsd), std.error(sh_2dsd))## # A tibble: 2 x 4
## group `n()` `mean(sh_2dsd)` `std.error(sh_2dsd)`
## <fctr> <int> <dbl> <dbl>
## 1 control 22 44.74340 0.4990321
## 2 stress 22 42.74079 0.6472687bartlett.test(sh_2dsd ~ group, data=d_2dsd)##
## Bartlett test of homogeneity of variances
##
## data: sh_2dsd by group
## Bartlett's K-squared = 1.3722, df = 1, p-value = 0.2414t.test(sh_2dsd ~ group, data=d_2dsd, var.equal=TRUE)##
## Two Sample t-test
##
## data: sh_2dsd by group
## t = 2.4503, df = 42, p-value = 0.01852
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.353220 3.652002
## sample estimates:
## mean in group control mean in group stress
## 44.74340 42.74079summary(lm(sh_2dsd ~ sh_count, data=d_2dsd))##
## Call:
## lm(formula = sh_2dsd ~ sh_count, data = d_2dsd)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2551 -2.0301 0.0787 2.4672 6.5438
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42.82125 0.95190 44.985 <2e-16 ***
## sh_count 0.01366 0.01259 1.085 0.284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.858 on 42 degrees of freedom
## Multiple R-squared: 0.02726, Adjusted R-squared: 0.004099
## F-statistic: 1.177 on 1 and 42 DF, p-value: 0.2842# num of SHs doesn't predict 2dsd
# does this hold when removing poor localizer subjs?
subids_rm = c('ap168', 'ap174') # bad VTC localizer classification
d_2dsd %>% filter(!subid %in% subids_rm) %>% group_by(group) %>% summarise(n(), mean(sh_2dsd), std.error(sh_2dsd))## # A tibble: 2 x 4
## group `n()` `mean(sh_2dsd)` `std.error(sh_2dsd)`
## <fctr> <int> <dbl> <dbl>
## 1 control 22 44.74340 0.4990321
## 2 stress 20 42.88347 0.6761959bartlett.test(sh_2dsd ~ group, data=d_2dsd %>% filter(!subid %in% subids_rm))##
## Bartlett test of homogeneity of variances
##
## data: sh_2dsd by group
## Bartlett's K-squared = 1.274, df = 1, p-value = 0.259t.test(sh_2dsd ~ group, data=d_2dsd %>% filter(!subid %in% subids_rm), var.equal=TRUE)##
## Two Sample t-test
##
## data: sh_2dsd by group
## t = 2.2404, df = 40, p-value = 0.03069
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1820817 3.5377809
## sample estimates:
## mean in group control mean in group stress
## 44.74340 42.883477a) Does VTC variability track HC assoc accuracy?
dat = read.csv('/Volumes/group/awagner/sgagnon/AP/results/df_sourceAcc.csv')
dat %>% group_by(subid, group) %>%
summarise(mean_acc = mean(mean_acc), n()) %>%
group_by(group) %>%
summarise(mean(mean_acc), n())## # A tibble: 2 x 3
## group `mean(mean_acc)` `n()`
## <fctr> <dbl> <int>
## 1 control 0.8185684 23
## 2 stress 0.7387830 23dat## X subid group mean_acc sum count
## 1 1 ap100 control 0.8725490 89 102
## 2 2 ap101 control 0.9375000 105 112
## 3 3 ap102 control 0.7010309 68 97
## 4 4 ap103 control 0.8968254 113 126
## 5 5 ap104 control 0.9130435 147 161
## 6 6 ap105 control 0.8623188 119 138
## 7 7 ap106 control 0.5087719 29 57
## 8 8 ap107 control 0.8802817 125 142
## 9 9 ap108 control 0.7073171 29 41
## 10 10 ap109 control 0.6489362 61 94
## 11 11 ap110 control 0.9523810 20 21
## 12 12 ap111 control 0.9012346 73 81
## 13 13 ap113 control 0.8152174 75 92
## 14 14 ap114 control 0.9357798 102 109
## 15 15 ap115 control 0.7428571 78 105
## 16 16 ap116 control 0.9610390 148 154
## 17 17 ap117 control 0.7456140 85 114
## 18 18 ap118 control 0.7682927 63 82
## 19 19 ap119 control 0.8984375 115 128
## 20 20 ap120 control 0.8522727 75 88
## 21 21 ap121 control 0.6415094 34 53
## 22 22 ap122 control 0.8981481 97 108
## 23 23 ap150 stress 0.8800000 66 75
## 24 24 ap152 stress 0.8461538 44 52
## 25 25 ap153 stress 0.7173913 33 46
## 26 26 ap154 stress 0.7931034 46 58
## 27 27 ap155 stress 0.7169811 38 53
## 28 28 ap156 stress 0.3125000 5 16
## 29 29 ap157 stress 0.7794118 53 68
## 30 30 ap158 control 0.7857143 11 14
## 31 31 ap159 stress 0.8360656 51 61
## 32 32 ap160 stress 0.6538462 51 78
## 33 33 ap161 stress 0.5192308 27 52
## 34 34 ap162 stress 0.6567164 44 67
## 35 35 ap163 stress 0.9145299 107 117
## 36 36 ap164 stress 0.9318182 82 88
## 37 37 ap165 stress 0.9154930 65 71
## 38 38 ap166 stress 0.8000000 72 90
## 39 39 ap167 stress 0.6724138 39 58
## 40 40 ap168 stress 0.5000000 46 92
## 41 41 ap169 stress 0.6826923 71 104
## 42 42 ap170 stress 0.9250000 74 80
## 43 43 ap171 stress 0.8048780 33 41
## 44 44 ap172 stress 0.7777778 21 27
## 45 45 ap173 stress 0.4705882 8 17
## 46 46 ap174 stress 0.8854167 85 96hist(dat$mean_acc)
dat = dat %>% mutate(mean_acc_logit = car::logit(mean_acc))d_2dsd = dat %>% right_join(d_2dsd, by = c('subid', 'group'))## Warning in right_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vectord_2dsd %>% group_by(group) %>% summarise(n(), mean(sh_2dsd), std.error(sh_2dsd))## # A tibble: 2 x 4
## group `n()` `mean(sh_2dsd)` `std.error(sh_2dsd)`
## <fctr> <int> <dbl> <dbl>
## 1 control 22 44.74340 0.4990321
## 2 stress 22 42.74079 0.6472687str(d_2dsd)## 'data.frame': 44 obs. of 10 variables:
## $ X.x : int 1 2 3 4 5 6 8 9 10 11 ...
## $ subid : chr "ap100" "ap101" "ap102" "ap103" ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## $ mean_acc : num 0.873 0.938 0.701 0.897 0.913 ...
## $ sum : int 89 105 68 113 147 119 125 29 61 20 ...
## $ count : int 102 112 97 126 161 138 142 41 94 21 ...
## $ mean_acc_logit: num 1.924 2.708 0.852 2.162 2.351 ...
## $ X.y : int 0 1 2 3 4 5 6 7 8 9 ...
## $ sh_count : num 88 102 66 113 146 119 124 28 61 20 ...
## $ sh_2dsd : num 45.1 45.3 48.4 42.5 44.9 ...head(d_2dsd)## X.x subid group mean_acc sum count mean_acc_logit X.y sh_count
## 1 1 ap100 control 0.8725490 89 102 1.9236870 0 88
## 2 2 ap101 control 0.9375000 105 112 2.7080502 1 102
## 3 3 ap102 control 0.7010309 68 97 0.8522119 2 66
## 4 4 ap103 control 0.8968254 113 126 2.1624385 3 113
## 5 5 ap104 control 0.9130435 147 161 2.3513753 4 146
## 6 6 ap105 control 0.8623188 119 138 1.8346845 5 119
## sh_2dsd
## 1 45.11144
## 2 45.31822
## 3 48.41050
## 4 42.47459
## 5 44.87312
## 6 41.98806plot(mean_acc_logit ~ sh_2dsd, data=d_2dsd)
fit1 = lm(mean_acc_logit ~ sh_2dsd, data=d_2dsd)
fit2 = lm(mean_acc_logit ~ poly(sh_2dsd,2), data=d_2dsd)
anova(fit1, fit2) # quad is better, but looks like driven by 2 outliers## Analysis of Variance Table
##
## Model 1: mean_acc_logit ~ sh_2dsd
## Model 2: mean_acc_logit ~ poly(sh_2dsd, 2)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 42 24.033
## 2 41 19.968 1 4.0649 8.3467 0.006147 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(fit1)##
## Call:
## lm(formula = mean_acc_logit ~ sh_2dsd, data = d_2dsd)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.66319 -0.58423 -0.03954 0.60094 1.63512
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.53252 1.76549 -2.001 0.05190 .
## sh_2dsd 0.11595 0.04028 2.879 0.00625 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7564 on 42 degrees of freedom
## Multiple R-squared: 0.1648, Adjusted R-squared: 0.1449
## F-statistic: 8.287 on 1 and 42 DF, p-value: 0.006255plot(fit1)
# holds when controlling for group?
contrasts(d_2dsd$group) = c(-1,1)
summary(lm(mean_acc_logit ~ sh_2dsd + group, data=d_2dsd))##
## Call:
## lm(formula = mean_acc_logit ~ sh_2dsd + group, data = d_2dsd)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.69814 -0.45212 0.05515 0.59394 1.49419
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.73586 1.87703 -1.458 0.1526
## sh_2dsd 0.09774 0.04283 2.282 0.0278 *
## group1 -0.14581 0.12127 -1.202 0.2361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7525 on 41 degrees of freedom
## Multiple R-squared: 0.1932, Adjusted R-squared: 0.1539
## F-statistic: 4.91 on 2 and 41 DF, p-value: 0.01225# what about removing VTC outliers?
plot(mean_acc_logit ~ sh_2dsd, data=d_2dsd %>% filter(!subid %in% subids_rm))
summary(lm(mean_acc_logit ~ sh_2dsd, data=d_2dsd %>% filter(!subid %in% subids_rm)))##
## Call:
## lm(formula = mean_acc_logit ~ sh_2dsd, data = d_2dsd %>% filter(!subid %in%
## subids_rm))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.58105 -0.52582 -0.09643 0.63637 1.62629
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.83575 1.84242 -1.539 0.1316
## sh_2dsd 0.10032 0.04192 2.393 0.0215 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7559 on 40 degrees of freedom
## Multiple R-squared: 0.1252, Adjusted R-squared: 0.1033
## F-statistic: 5.725 on 1 and 40 DF, p-value: 0.02151Does 2dsd explain variance in hipp -> sourceAcc not explained by VTC?
vtc = read.csv('/Volumes/group/awagner/sgagnon/AP/results/sourcehit_avglogit_vtc.csv')
vtc = vtc %>% mutate(mean_logit = mean) %>% dplyr::select(-mean)
d_2dsd = read.csv('/Volumes/group/awagner/sgagnon/AP/results/retrieval_2dsd_SH_bilat-parahipp_fusi_inftemp_nohipp.csv')
roi = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/pe_sourcehit-cr_zstat1_peak1_5mm_sphere_masked.nii.csv')
roi = roi %>% filter(cond %in% c('sourcehit', 'CR')) %>%
mutate(cond = factor(cond)) %>%
dplyr::select(subid, group, cond, value) %>% # calc diff score
spread(key = cond, value=value) %>%
mutate(diff = sourcehit - CR) %>%
right_join(d_2dsd, by = c('subid', 'group')) %>%
right_join(vtc, by = c('subid', 'group')) %>%
left_join(dat, by = c('subid', 'group')) %>%
mutate(diff_orig = diff,
diff = as.numeric(scale(diff)),
mean_logit = as.numeric(scale(mean_logit)),
mean_acc = as.numeric(scale(car::logit(mean_acc))),
sh_2dsd = as.numeric(scale(sh_2dsd)))## Warning in right_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vector## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vectordim(roi)## [1] 42 16str(roi)## 'data.frame': 42 obs. of 16 variables:
## $ subid : chr "ap100" "ap101" "ap102" "ap103" ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## $ CR : num -396 320 107 -519 -416 ...
## $ sourcehit : num 379.9 990.6 891.5 90.7 -99.2 ...
## $ diff : num 0.924 0.652 0.945 0.495 -0.263 ...
## $ X.x : int 0 1 2 3 4 5 6 7 8 9 ...
## $ sh_count : num 88 102 66 113 146 119 124 28 61 20 ...
## $ sh_2dsd : num 0.445 0.519 1.617 -0.491 0.361 ...
## $ X.y : int 1 2 3 4 5 6 7 8 9 10 ...
## $ mean_logit : num 0.3483 -0.2448 0.7178 -0.8012 -0.0116 ...
## $ X : int 1 2 3 4 5 6 8 9 10 11 ...
## $ mean_acc : num 0.451 1.433 -0.892 0.75 0.987 ...
## $ sum : int 89 105 68 113 147 119 125 29 61 20 ...
## $ count : int 102 112 97 126 161 138 142 41 94 21 ...
## $ mean_acc_logit: num 1.924 2.708 0.852 2.162 2.351 ...
## $ diff_orig : num 776 671 784 610 317 ...contrasts(roi$group) = c(1,-1)
summary(lm(mean_acc ~ mean_logit + diff + sh_2dsd, data=roi))##
## Call:
## lm(formula = mean_acc ~ mean_logit + diff + sh_2dsd, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.87604 -0.34214 -0.07273 0.53240 1.86639
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.794e-16 1.293e-01 0.000 1.00000
## mean_logit -8.283e-02 1.625e-01 -0.510 0.61318
## diff 5.130e-01 1.533e-01 3.347 0.00185 **
## sh_2dsd 3.115e-01 1.403e-01 2.220 0.03248 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8378 on 38 degrees of freedom
## Multiple R-squared: 0.3494, Adjusted R-squared: 0.298
## F-statistic: 6.803 on 3 and 38 DF, p-value: 0.0008786summary(lm(mean_logit ~ sh_2dsd, data=roi))##
## Call:
## lm(formula = mean_logit ~ sh_2dsd, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.50446 -0.42701 0.06335 0.58201 2.12063
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.607e-16 1.459e-01 0.000 1.0000
## sh_2dsd 3.572e-01 1.477e-01 2.419 0.0202 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9456 on 40 degrees of freedom
## Multiple R-squared: 0.1276, Adjusted R-squared: 0.1058
## F-statistic: 5.851 on 1 and 40 DF, p-value: 0.02021# Is hipp effect on source acc mediated by 2dsd?
contrasts(roi$group) = c(1,-1)
model.t = lm(mean_acc ~ diff, data=roi); summary(model.t) ##
## Call:
## lm(formula = mean_acc ~ diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.24505 -0.45864 -0.00737 0.52330 1.87362
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.116e-16 1.340e-01 0.000 1.000000
## diff 5.138e-01 1.356e-01 3.788 0.000501 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8686 on 40 degrees of freedom
## Multiple R-squared: 0.264, Adjusted R-squared: 0.2456
## F-statistic: 14.35 on 1 and 40 DF, p-value: 0.0005012# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -2.593e-17 1.340e-01 0.000 1.000000
# diff 5.138e-01 1.356e-01 3.788 0.000501 ***
model.m = lm(sh_2dsd ~ diff, data=roi); summary(model.m)##
## Call:
## lm(formula = sh_2dsd ~ diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4071 -0.7691 0.1440 0.7134 2.4809
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.713e-16 1.547e-01 0.000 1.000
## diff 1.403e-01 1.565e-01 0.896 0.375
##
## Residual standard error: 1.002 on 40 degrees of freedom
## Multiple R-squared: 0.01969, Adjusted R-squared: -0.004819
## F-statistic: 0.8034 on 1 and 40 DF, p-value: 0.3755# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 8.713e-16 1.547e-01 0.000 1.000
# diff 1.403e-01 1.565e-01 0.896 0.375
model.y = lm(mean_acc ~ sh_2dsd + diff, data=roi); summary(model.y)##
## Call:
## lm(formula = mean_acc ~ sh_2dsd + diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8909 -0.3753 -0.1001 0.4779 1.8724
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.620e-16 1.280e-01 0.000 1.000000
## sh_2dsd 2.874e-01 1.309e-01 2.196 0.034119 *
## diff 4.735e-01 1.309e-01 3.617 0.000844 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8298 on 39 degrees of freedom
## Multiple R-squared: 0.345, Adjusted R-squared: 0.3114
## F-statistic: 10.27 on 2 and 39 DF, p-value: 0.0002614# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -2.764e-16 1.280e-01 0.000 1.000000
# sh_2dsd 2.874e-01 1.309e-01 2.196 0.034119 *
# diff 4.735e-01 1.309e-01 3.617 0.000844 ***
model.other = lm(mean_acc ~ sh_2dsd, data=roi); summary(model.other)##
## Call:
## lm(formula = mean_acc ~ sh_2dsd, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9807 -0.6587 -0.1208 0.7972 2.0374
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.119e-16 1.461e-01 0.000 1.0000
## sh_2dsd 3.539e-01 1.479e-01 2.393 0.0215 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9469 on 40 degrees of freedom
## Multiple R-squared: 0.1252, Adjusted R-squared: 0.1033
## F-statistic: 5.725 on 1 and 40 DF, p-value: 0.02151# (Intercept) -3.263e-16 1.461e-01 0.000 1.0000
# sh_2dsd 3.539e-01 1.479e-01 2.393 0.0215 *
ggplot(roi, aes(x=sh_2dsd, y=mean_acc)) +
geom_point() +
geom_smooth(method=lm) +
xlab('SH 2dsd') +
ylab('Source accuracy (logit)')
roi %>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 22
## 2 stress 20## Bootstrapping Confidence Interval (BCa and Percentile)
boot.med <- function(data, indices){
data <-data[indices,] #this does the random resampling of rows
reg1 <- lm(sh_2dsd ~ diff, data=data)
reg2 <- lm(mean_acc ~ sh_2dsd + diff, data=data)
a <- coefficients(reg1)["diff"] #the a path coefficient
b <- coefficients(reg2)["sh_2dsd"] #the b path coefficient
a*b #returns the product of a*b
}
medboot<-boot(data=roi, statistic = boot.med, R=5000)
mean(medboot$t)## [1] 0.05266069quantile(medboot$t, c(0.025, 0.975))## 2.5% 97.5%
## -0.08500636 0.24561459# > mean(medboot$t)
# [1] 0.05026415
# > quantile(medboot$t, c(0.025, 0.975))
# 2.5% 97.5%
# -0.08661536 0.23665193 Supplemental stats
S1) Encoding performance
Encoding: RT
d = ds %>%
filter(resp != 'no response') %>%
mutate(resp = factor(resp)) %>%
group_by(group, subid, repCount, repType, cond) %>%
summarise(rt = median_(respRT)) %>%
ungroup()
res = lmer(rt ~ (cond + scale(repCount) * repType) * group +
(1 + cond + scale(repCount) * repType|subid), data=d,
control=lmerControl(optCtrl=list(maxfun=1e5)), REML=TRUE)
summary(res)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: rt ~ (cond + scale(repCount) * repType) * group + (1 + cond +
## scale(repCount) * repType | subid)
## Data: d
## Control: lmerControl(optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: -300.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5719 -0.4774 0.0681 0.5825 2.6085
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 7.269e-02 0.269618
## cond1 1.833e-05 0.004281 -0.58
## scale(repCount) 2.313e-02 0.152097 0.33 0.58
## repType1 4.070e-03 0.063797 -0.67 -0.22 -0.92
## scale(repCount):repType1 5.547e-03 0.074481 -0.66 -0.22 -0.92
## Residual 1.952e-02 0.139725
##
##
##
##
##
## 1.00
##
## Number of obs: 564, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.602587 0.040419 45.000000 39.650
## cond1 -0.003430 0.005918 383.000000 -0.580
## scale(repCount) -0.239199 0.024956 45.500000 -9.585
## repType1 -0.001237 0.013150 54.200000 -0.094
## group1 0.029139 0.040419 45.000000 0.721
## scale(repCount):repType1 0.052630 0.015763 57.500000 3.339
## cond1:group1 -0.001723 0.005918 383.000000 -0.291
## scale(repCount):group1 0.020218 0.024956 45.500000 0.810
## repType1:group1 -0.016456 0.013150 54.200000 -1.251
## scale(repCount):repType1:group1 -0.010379 0.015763 57.500000 -0.658
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## cond1 0.56256
## scale(repCount) 1.73e-12 ***
## repType1 0.92539
## group1 0.47468
## scale(repCount):repType1 0.00148 **
## cond1:group1 0.77105
## scale(repCount):group1 0.42209
## repType1:group1 0.21616
## scale(repCount):repType1:group1 0.51290
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cond1 scl(C) rpTyp1 group1 sc(C):T1 cnd1:1 s(C):1
## cond1 -0.060
## scal(rpCnt) 0.353 0.054
## repType1 -0.570 -0.016 -0.817
## group1 -0.021 0.001 -0.008 0.012
## scl(rpC):T1 -0.567 -0.016 -0.838 0.827 0.012
## cond1:grop1 0.001 -0.021 -0.001 0.000 -0.060 0.000
## scl(rpCn):1 -0.008 -0.001 -0.021 0.017 0.353 0.018 0.054
## rpTyp1:grp1 0.012 0.000 0.017 -0.021 -0.570 -0.018 -0.016 -0.817
## scl(C):T1:1 0.012 0.000 0.018 -0.018 -0.567 -0.021 -0.016 -0.838
## rpT1:1
## cond1
## scal(rpCnt)
## repType1
## group1
## scl(rpC):T1
## cond1:grop1
## scl(rpCn):1
## rpTyp1:grp1
## scl(C):T1:1 0.827summary(lmer(rt ~ (cond + scale(repCount)) * group +
(1 + scale(repCount)|subid), # remove cond to converge
data=d %>% filter(repType == '2'),
control=lmerControl(optCtrl=list(maxfun=1e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: rt ~ (cond + scale(repCount)) * group + (1 + scale(repCount) |
## subid)
## Data: d %>% filter(repType == "2")
## Control: lmerControl(optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: -113.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.90854 -0.56696 -0.02986 0.52469 2.07304
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.071518 0.26743
## scale(repCount) 0.013333 0.11547 0.17
## Residual 0.005605 0.07487
## Number of obs: 188, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.785967 0.039398 45.000000 45.332 < 2e-16 ***
## cond1 -0.004509 0.005462 92.000000 -0.826 0.411
## scale(repCount) -0.136972 0.017714 45.000000 -7.732 8.48e-10 ***
## group1 0.026499 0.039398 45.000000 0.673 0.505
## cond1:group1 0.005357 0.005462 92.000000 0.981 0.329
## scale(repCount):group1 0.014360 0.017714 45.000000 0.811 0.422
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cond1 scl(C) group1 cnd1:1
## cond1 0.000
## scal(rpCnt) 0.159 0.000
## group1 -0.021 0.000 -0.003
## cond1:grop1 0.000 -0.021 0.000 0.000
## scl(rpCn):1 -0.003 0.000 -0.021 0.159 0.000summary(lmer(rt ~ (cond + scale(repCount)) * group +
(1 + scale(repCount)|subid), # remove cond to converge
data=d %>% filter(repType == '4'),
control=lmerControl(optCtrl=list(maxfun=1e5))))## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: rt ~ (cond + scale(repCount)) * group + (1 + scale(repCount) |
## subid)
## Data: d %>% filter(repType == "4")
## Control: lmerControl(optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: -84.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.92638 -0.55847 0.08084 0.57011 2.36995
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.051025 0.22589
## scale(repCount) 0.007068 0.08407 -0.08
## Residual 0.026193 0.16184
## Number of obs: 376, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.543127 0.033997 45.000000 45.390 <2e-16
## cond1 -0.002890 0.008348 280.000000 -0.346 0.729
## scale(repCount) -0.195545 0.014844 45.000000 -13.173 <2e-16
## group1 0.015753 0.033997 45.000000 0.463 0.645
## cond1:group1 -0.005264 0.008348 280.000000 -0.631 0.529
## scale(repCount):group1 0.010312 0.014844 45.000000 0.695 0.491
##
## (Intercept) ***
## cond1
## scale(repCount) ***
## group1
## cond1:group1
## scale(repCount):group1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cond1 scl(C) group1 cnd1:1
## cond1 0.000
## scal(rpCnt) -0.064 0.000
## group1 -0.021 0.000 0.001
## cond1:grop1 0.000 -0.021 0.000 0.000
## scl(rpCn):1 0.001 0.000 -0.021 -0.064 0.000S2) Compare item vs. assoc dprime (Wixted approach)
item_d = read.csv('/Volumes/group/awagner/sgagnon/AP/results/behav_item_dprime.csv')
assoc_d = read.csv('/Volumes/group/awagner/sgagnon/AP/results/behav_assoc_dprime.csv')
# merge these 2 measures:
item_d = item_d %>%
dplyr::select(subid, group, dprime) %>%
group_by(subid, group) %>%
summarise(item_d = mean(dprime))
assoc_d = assoc_d %>%
dplyr::select(subid, group, dprime) %>%
group_by(subid, group) %>%
summarise(assoc_d = mean(dprime))
combined_d = item_d %>% left_join(assoc_d)## Joining, by = c("subid", "group")combined_d## Source: local data frame [47 x 4]
## Groups: subid [?]
##
## # A tibble: 47 x 4
## subid group item_d assoc_d
## <fctr> <fctr> <dbl> <dbl>
## 1 ap100 control 1.6545387 1.5923038
## 2 ap101 control 2.8518891 2.0702869
## 3 ap102 control 1.2563972 0.7773663
## 4 ap103 control 2.5178107 2.0206085
## 5 ap104 control 3.7519274 2.7741996
## 6 ap105 control 2.8281077 1.8557002
## 7 ap106 control 1.3389959 0.1081459
## 8 ap107 control 2.0413809 2.0035682
## 9 ap108 control 0.6659922 0.4459195
## 10 ap109 control 1.1989806 0.5196194
## # ... with 37 more rows## Wixted 2004 standardized measure:
# make sure no major outliers biasing sd!
boxplot(combined_d %>% filter(group == 'control') %>%
dplyr::select(item_d, assoc_d))## Adding missing grouping variables: `subid`stripchart(combined_d %>% filter(group == 'control') %>%
dplyr::select(item_d, assoc_d), vertical = TRUE, method = "jitter", add=TRUE)## Adding missing grouping variables: `subid`
# get control group stats for standardizing
sum_stats = combined_d %>% filter(group == 'control') %>%
ungroup() %>%
dplyr::select(item_d, assoc_d) %>%
summarise_each(funs(mean, sd, n()))
sum_stats## # A tibble: 1 x 6
## item_d_mean assoc_d_mean item_d_sd assoc_d_sd item_d_n assoc_d_n
## <dbl> <dbl> <dbl> <dbl> <int> <int>
## 1 1.894499 1.366334 0.8853859 0.7938154 23 23# get stress standardized by control stats
rel_control = combined_d %>%
ungroup() %>%
mutate(item_z = (item_d - sum_stats$item_d_mean)/sum_stats$item_d_sd,
assoc_z = (assoc_d - sum_stats$assoc_d_mean)/sum_stats$assoc_d_sd) %>%
filter(group == 'stress') %>%
dplyr::select(subid, group, item_z, assoc_z) %>%
gather(key='dprimetype', value='zscore', item_z:assoc_z)
rel_control %>%
group_by(dprimetype) %>%
summarise_each(funs(mean))## Warning in mean.default(structure(c(23L, 24L, 25L, 26L, 27L, 28L, 29L,
## 30L, : argument is not numeric or logical: returning NA
## Warning in mean.default(structure(c(23L, 24L, 25L, 26L, 27L, 28L, 29L,
## 30L, : argument is not numeric or logical: returning NA## Warning in mean.default(structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, :
## argument is not numeric or logical: returning NA
## Warning in mean.default(structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, :
## argument is not numeric or logical: returning NA## # A tibble: 2 x 4
## dprimetype subid group zscore
## <chr> <lgl> <lgl> <dbl>
## 1 assoc_z NA NA -0.7129529
## 2 item_z NA NA -0.4695853# stats:
bartlett.test(zscore ~ dprimetype, data=rel_control)##
## Bartlett test of homogeneity of variances
##
## data: zscore by dprimetype
## Bartlett's K-squared = 0.47665, df = 1, p-value = 0.4899t.test(zscore ~ dprimetype, data=rel_control, var.equal=TRUE, paired=TRUE)##
## Paired t-test
##
## data: zscore by dprimetype
## t = -1.9115, df = 23, p-value = 0.06848
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.5067463 0.0200110
## sample estimates:
## mean of the differences
## -0.2433676S3) Associative accuracy (behavioral inclusion criteria) by encoding strength and run type (safe, threat)
High confidence assoc accuracy
dat = dtest %>%
filter(subj_status == 'old',
reps > 0,
conf == 'Hi') %>%
group_by(subid, group) %>%
summarise(mean_acc = mean(acc_num), sum=sum(acc_num), count=n()) %>%
ungroup() %>%
complete(nesting(subid, group), fill = list(mean_acc=0,
count=0))
dim(dat) # all subjs included## [1] 47 5dat %>% filter(count < 6) ## # A tibble: 1 x 5
## subid group mean_acc sum count
## <fctr> <fctr> <dbl> <dbl> <dbl>
## 1 ap151 stress 0.6 3 5# ap151
rm_subids = dat %>% filter(count < 6) %>% pull(subid) %>% unique(); rm_subids## [1] ap151
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174rm_subids # ap151## [1] ap151
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174dat = dat %>% filter(!subid %in% rm_subids)
dat %>% group_by(subid, group) %>%
summarise(mean_acc = mean(mean_acc), n()) %>%
group_by(group) %>%
summarise(mean(mean_acc), n())## # A tibble: 2 x 3
## group `mean(mean_acc)` `n()`
## <fctr> <dbl> <int>
## 1 control 0.8185684 23
## 2 stress 0.7387830 23hist(dat$mean_acc) # skewed negatively
# logit transform
dat = dat %>% mutate(mean_acc_logit = car::logit(mean_acc))
hist(dat$mean_acc_logit)
boxplot(dat$mean_acc_logit)
# stats:
dat %>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 23
## 2 stress 23bartlett.test(mean_acc_logit ~ group, data=dat)##
## Bartlett test of homogeneity of variances
##
## data: mean_acc_logit by group
## Bartlett's K-squared = 0.14041, df = 1, p-value = 0.7079t.test(mean_acc_logit ~ group, data=dat, var.equal=TRUE)##
## Two Sample t-test
##
## data: mean_acc_logit by group
## t = 1.9585, df = 44, p-value = 0.05653
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01457236 1.01850230
## sample estimates:
## mean in group control mean in group stress
## 1.706974 1.205009dat_control = dat %>% filter(group == 'control')
t.test(x=dat_control$mean_acc_logit, mu = 0)##
## One Sample t-test
##
## data: dat_control$mean_acc_logit
## t = 9.8233, df = 22, p-value = 1.668e-09
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 1.346600 2.067347
## sample estimates:
## mean of x
## 1.706974dat_control = dat %>% filter(group == 'stress')
t.test(x=dat_control$mean_acc_logit, mu = 0)##
## One Sample t-test
##
## data: dat_control$mean_acc_logit
## t = 6.3961, df = 22, p-value = 1.953e-06
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.8142949 1.5957230
## sample estimates:
## mean of x
## 1.205009# logit(.5) = 0 # when acc=50%, logit = 0P(correct | “high conf old” to old item), broken down by study reps
with(dtest %>%
filter(subj_status == 'old',
reps > 0,
conf == 'Hi'), table(acc, acc_num))## acc_num
## acc 0 1
## CR 0 0
## FA 0 0
## H 0 3025
## M 0 0
## no response 0 0
## SM 706 0# just split by study reps
dat = dtest %>%
filter(subj_status == 'old',
reps > 0,
conf == 'Hi') %>%
group_by(subid, group, reps) %>%
summarise(mean_acc = mean(acc_num), count=n()) %>%
ungroup() %>%
mutate(reps = factor(reps)) %>%
complete(nesting(subid, group), reps, fill = list(mean_acc=0,
count=0))
dim(dat) # all subjs included## [1] 94 5dat %>% filter(count < 6) ## # A tibble: 4 x 5
## subid group reps mean_acc count
## <fctr> <fctr> <fctr> <dbl> <dbl>
## 1 ap110 control 2 0.75 4
## 2 ap151 stress 2 0.00 1
## 3 ap151 stress 4 0.75 4
## 4 ap158 control 2 0.50 4dat## # A tibble: 94 x 5
## subid group reps mean_acc count
## <fctr> <fctr> <fctr> <dbl> <dbl>
## 1 ap100 control 2 0.9090909 44
## 2 ap100 control 4 0.8448276 58
## 3 ap101 control 2 0.8444444 45
## 4 ap101 control 4 1.0000000 67
## 5 ap102 control 2 0.6388889 36
## 6 ap102 control 4 0.7377049 61
## 7 ap103 control 2 0.8727273 55
## 8 ap103 control 4 0.9154930 71
## 9 ap104 control 2 0.8607595 79
## 10 ap104 control 4 0.9634146 82
## # ... with 84 more rowsrm_subids = dat %>% filter(count < 6) %>% pull(subid) %>% unique(); rm_subids## [1] ap110 ap151 ap158
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174# 2 control, 1 stress
dat = dat %>% filter(!subid %in% rm_subids)
dat## # A tibble: 88 x 5
## subid group reps mean_acc count
## <fctr> <fctr> <fctr> <dbl> <dbl>
## 1 ap100 control 2 0.9090909 44
## 2 ap100 control 4 0.8448276 58
## 3 ap101 control 2 0.8444444 45
## 4 ap101 control 4 1.0000000 67
## 5 ap102 control 2 0.6388889 36
## 6 ap102 control 4 0.7377049 61
## 7 ap103 control 2 0.8727273 55
## 8 ap103 control 4 0.9154930 71
## 9 ap104 control 2 0.8607595 79
## 10 ap104 control 4 0.9634146 82
## # ... with 78 more rowsdat %>% group_by(subid, group) %>% summarise(n()) %>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 21
## 2 stress 23dat = dat%>% mutate(mean_logit = car::logit(mean_acc))## Warning in car::logit(c(0.909090909090909, 0.844827586206897,
## 0.844444444444444, : proportions remapped to (0.025, 0.975)contrasts(dat$reps) = c(-1,1); contrasts(dat$reps)## [,1]
## 2 -1
## 4 1contrasts(dat$group) = c(1,-1); contrasts(dat$group)## [,1]
## control 1
## stress -1summary(lmer(mean_logit ~ group*reps +
(1|subid), data=dat)) # not enough obs for the RE## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: mean_logit ~ group * reps + (1 | subid)
## Data: dat
##
## REML criterion at convergence: 179.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.04808 -0.46015 0.01082 0.44168 2.70514
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.5407 0.7353
## Residual 0.1326 0.3641
## Number of obs: 88, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.30047 0.11757 42.00000 11.061 5.06e-14 ***
## group1 0.21906 0.11757 42.00000 1.863 0.069437 .
## reps1 0.15390 0.03886 42.00000 3.961 0.000283 ***
## group1:reps1 0.02946 0.03886 42.00000 0.758 0.452555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 reps1
## group1 0.045
## reps1 0.000 0.000
## group1:rps1 0.000 0.000 0.045P(correct | “high conf old” to old item), broken down by shock condition
# just split by study reps
dat = dtest %>%
filter(subj_status == 'old',
reps > 0,
conf == 'Hi') %>%
group_by(subid, group, shockCond) %>%
summarise(mean_acc = mean(acc_num), count=n()) %>%
ungroup() %>%
complete(nesting(subid, group), shockCond, fill = list(mean_acc=0,
count=0))
dim(dat) # all subjs included## [1] 94 5dat %>% filter(count < 6) ## # A tibble: 4 x 5
## subid group shockCond mean_acc count
## <fctr> <fctr> <fctr> <dbl> <dbl>
## 1 ap151 stress safe 0.50 4
## 2 ap151 stress threat 1.00 1
## 3 ap156 stress safe 0.00 4
## 4 ap173 stress threat 0.25 4# many low trial counts/bin!
rm_subids = dat %>% filter(count < 6) %>% pull(subid) %>% unique()
dat = dat %>% filter(!subid %in% rm_subids)
dat %>% group_by(subid, group) %>% summarise(n()) %>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 23
## 2 stress 21dat = dat%>% mutate(mean_logit = car::logit(mean_acc))## Warning in car::logit(c(0.901960784313726, 0.843137254901961,
## 0.947368421052632, : proportions remapped to (0.025, 0.975)contrasts(dat$shockCond) = c(1,-1); contrasts(dat$shockCond)## [,1]
## safe 1
## threat -1contrasts(dat$group) = c(1,-1); contrasts(dat$group)## [,1]
## control 1
## stress -1summary(lmer(mean_logit ~ group*shockCond +
(1|subid), data=dat)) # not enough obs for the RE## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: mean_logit ~ group * shockCond + (1 | subid)
## Data: dat
##
## REML criterion at convergence: 206.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.67278 -0.44126 -0.06186 0.46657 3.14956
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.3756 0.6128
## Residual 0.2922 0.5405
## Number of obs: 88, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.45029 0.10900 42.00000 13.306 <2e-16 ***
## group1 0.16350 0.10900 42.00000 1.500 0.141
## shockCond1 -0.03931 0.05768 42.00000 -0.681 0.499
## group1:shockCond1 0.02327 0.05768 42.00000 0.403 0.689
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 shckC1
## group1 -0.045
## shockCond1 0.000 0.000
## grp1:shckC1 0.000 0.000 -0.045# what about just within stress
summary(lmer(mean_logit ~ shockCond +
(1|subid), data=dat %>% filter(group == 'stress'))) # not enough obs for the RE## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: mean_logit ~ shockCond + (1 | subid)
## Data: dat %>% filter(group == "stress")
##
## REML criterion at convergence: 88
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5070 -0.4060 0.0047 0.4600 2.2673
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.3802 0.6166
## Residual 0.1997 0.4469
## Number of obs: 42, groups: subid, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.28679 0.15119 20.00000 8.511 4.42e-08 ***
## shockCond1 -0.06258 0.06895 20.00000 -0.908 0.375
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## shockCond1 0.000Low confidence assoc accuracy
dat = dtest %>%
filter(subj_status == 'old',
reps > 0,
conf == 'Lo') %>%
group_by(subid, group) %>%
summarise(mean_acc = mean(acc_num), count=n()) %>%
ungroup() %>%
complete(nesting(subid, group), fill = list(mean_acc=0,
count=0))
dim(dat) # all subjs included## [1] 46 4dat %>% filter(count < 6) ## # A tibble: 3 x 4
## subid group mean_acc count
## <fctr> <fctr> <dbl> <dbl>
## 1 ap116 control 0.6666667 3
## 2 ap160 stress 1.0000000 1
## 3 ap169 stress 0.0000000 2# dat %>% filter(subid == 'ap156')
# many low trial counts/bin!
rm_subids = dat %>% filter(count < 6) %>% pull(subid) %>% unique(); rm_subids## [1] ap116 ap160 ap169
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174dat = dat %>% filter(!subid %in% rm_subids)
dat %>% group_by(subid, group) %>% summarise(n(), mean_acc = mean(mean_acc)) %>% group_by(group) %>% summarise(n(), mean(mean_acc))## # A tibble: 2 x 3
## group `n()` `mean(mean_acc)`
## <fctr> <int> <dbl>
## 1 control 21 0.5685041
## 2 stress 22 0.5152346hist(dat$mean_acc)
dat = dat%>% mutate(mean_acc_logit = car::logit(mean_acc))
hist(dat$mean_acc_logit)
bartlett.test(mean_acc_logit ~ group, data=dat)##
## Bartlett test of homogeneity of variances
##
## data: mean_acc_logit by group
## Bartlett's K-squared = 3.1647, df = 1, p-value = 0.07525t.test(mean_acc_logit ~ group, data=dat, var.equal=TRUE)##
## Two Sample t-test
##
## data: mean_acc_logit by group
## t = 2.5569, df = 41, p-value = 0.01436
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.04654901 0.39641939
## sample estimates:
## mean in group control mean in group stress
## 0.28273408 0.06124988dat_control = dat %>% filter(group == 'control')
t.test(x=dat_control$mean_acc_logit, mu = 0)##
## One Sample t-test
##
## data: dat_control$mean_acc_logit
## t = 3.8644, df = 20, p-value = 0.0009659
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.1301165 0.4353517
## sample estimates:
## mean of x
## 0.2827341dat_control = dat %>% filter(group == 'stress')
t.test(x=dat_control$mean_acc_logit, mu = 0)##
## One Sample t-test
##
## data: dat_control$mean_acc_logit
## t = 1.2805, df = 21, p-value = 0.2143
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.03822232 0.16072209
## sample estimates:
## mean of x
## 0.06124988dat## # A tibble: 43 x 5
## subid group mean_acc count mean_acc_logit
## <fctr> <fctr> <dbl> <dbl> <dbl>
## 1 ap100 control 0.4400000 25 -0.24116206
## 2 ap101 control 0.6666667 21 0.69314718
## 3 ap102 control 0.4285714 63 -0.28768207
## 4 ap103 control 0.5909091 22 0.36772478
## 5 ap105 control 0.7058824 17 0.87546874
## 6 ap106 control 0.6400000 75 0.57536414
## 7 ap107 control 0.6470588 17 0.60613580
## 8 ap108 control 0.6086957 69 0.44183275
## 9 ap109 control 0.5000000 46 0.00000000
## 10 ap110 control 0.5140187 107 0.05608947
## # ... with 33 more rowsP(correct | “low conf old” to old item), broken down by study reps
# just split by study reps
dat = dtest %>%
filter(subj_status == 'old',
reps > 0,
conf == 'Lo') %>%
group_by(subid, group, reps) %>%
summarise(mean_acc = mean(acc_num), count=n()) %>%
ungroup() %>%
mutate(reps = factor(reps)) %>%
complete(nesting(subid, group), reps, fill = list(mean_acc=0,
count=0))
47*2## [1] 94dim(dat)## [1] 92 5dat %>% filter(count < 6) ## # A tibble: 8 x 5
## subid group reps mean_acc count
## <fctr> <fctr> <fctr> <dbl> <dbl>
## 1 ap105 control 4 0.0 2
## 2 ap116 control 2 0.5 2
## 3 ap116 control 4 1.0 1
## 4 ap119 control 4 0.6 5
## 5 ap160 stress 2 1.0 1
## 6 ap160 stress 4 0.0 0
## 7 ap169 stress 2 0.0 0
## 8 ap169 stress 4 0.0 2# many low trial counts/bin!
rm_subids = dat %>% filter(count < 6) %>% pull(subid) %>% unique()
dat = dat %>% filter(!subid %in% rm_subids)
dat %>% group_by(subid, group) %>%summarise(n()) %>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 19
## 2 stress 22dat = dat%>% mutate(mean_logit = car::logit(mean_acc))
contrasts(dat$reps) = c(-1,1); contrasts(dat$reps)## [,1]
## 2 -1
## 4 1contrasts(dat$group) = c(1,-1); contrasts(dat$group)## [,1]
## control 1
## stress -1summary(lmer(mean_logit ~ group*reps +
(1|subid), data=dat)) # not enough obs for the RE## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: mean_logit ~ group * reps + (1 | subid)
## Data: dat
##
## REML criterion at convergence: 95.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.14047 -0.56911 -0.04094 0.64173 2.00488
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.0000 0.0000
## Residual 0.1597 0.3996
## Number of obs: 82, groups: subid, 41
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.16808 0.04424 78.00000 3.799 0.000286 ***
## group1 0.09690 0.04424 78.00000 2.190 0.031501 *
## reps1 0.06391 0.04424 78.00000 1.445 0.152577
## group1:reps1 0.03894 0.04424 78.00000 0.880 0.381439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 reps1
## group1 0.073
## reps1 0.000 0.000
## group1:rps1 0.000 0.000 0.073P(correct | “low conf old” to old item), broken down by shock condition
# just split by study reps
dat = dtest %>%
filter(subj_status == 'old',
reps > 0,
conf == 'Lo') %>%
group_by(subid, group, shockCond) %>%
summarise(mean_acc = mean(acc_num), count=n()) %>%
ungroup() %>%
complete(nesting(subid, group), shockCond, fill = list(mean_acc=0,
count=0))
dim(dat)## [1] 92 5dat %>% filter(count < 6) ## # A tibble: 6 x 5
## subid group shockCond mean_acc count
## <fctr> <fctr> <fctr> <dbl> <dbl>
## 1 ap116 control safe 0 1
## 2 ap116 control threat 1 2
## 3 ap160 stress safe 1 1
## 4 ap160 stress threat 0 0
## 5 ap169 stress safe 0 1
## 6 ap169 stress threat 0 1# many low trial counts/bin!
rm_subids = dat %>% filter(count < 6) %>% pull(subid) %>% unique()
dat = dat %>% filter(!subid %in% rm_subids)
dat %>% group_by(subid, group) %>%summarise(n()) %>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 21
## 2 stress 22dat = dat%>% mutate(mean_logit = car::logit(mean_acc))
contrasts(dat$shockCond) = c(1,-1); contrasts(dat$shockCond)## [,1]
## safe 1
## threat -1contrasts(dat$group) = c(1,-1); contrasts(dat$group)## [,1]
## control 1
## stress -1summary(lmer(mean_acc ~ group*shockCond +
(1|subid), data=dat)) # not enough obs for the RE## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: mean_acc ~ group * shockCond + (1 | subid)
## Data: dat
##
## REML criterion at convergence: -115.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.58296 -0.52143 0.03298 0.61129 2.59950
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.00000 0.000
## Residual 0.01146 0.107
## Number of obs: 86, groups: subid, 43
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.54464 0.01155 82.00000 47.171 <2e-16 ***
## group1 0.02533 0.01155 82.00000 2.194 0.031 *
## shockCond1 0.01711 0.01155 82.00000 1.482 0.142
## group1:shockCond1 0.01294 0.01155 82.00000 1.121 0.266
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 shckC1
## group1 0.023
## shockCond1 0.000 0.000
## grp1:shckC1 0.000 0.000 0.023# what about just within stress
summary(lmer(mean_logit ~ shockCond +
(1|subid), data=dat %>% filter(group == 'stress'))) # not enough obs for the RE## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: mean_logit ~ shockCond + (1 | subid)
## Data: dat %>% filter(group == "stress")
##
## REML criterion at convergence: 44
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.74325 -0.45006 -0.07833 0.59734 1.93537
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.0000 0.0000
## Residual 0.1395 0.3734
## Number of obs: 44, groups: subid, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.07879 0.05630 42.00000 1.400 0.169
## shockCond1 0.01945 0.05630 42.00000 0.345 0.731
##
## Correlation of Fixed Effects:
## (Intr)
## shockCond1 0.000S4) Localizer behavioral performance
P(correct)
## X run trial onset duration cond subcond resp acc respRT subid
## 1 0 1 1 12.0068 1.4946 object manmade manmade 1 0.8153 loc100
## 2 1 1 2 13.9136 1.0869 object manmade manmade 1 0.8784 loc100
## 3 2 1 3 15.0290 1.4716 object natural natural 1 0.6792 loc100
## 4 3 1 4 16.5293 1.4712 object manmade manmade 1 0.6867 loc100
## 5 4 1 5 18.0304 1.4701 object natural natural 1 0.5906 loc100
## 6 5 1 6 19.5308 1.4700 object natural natural 1 0.3726 loc100
## group duration_adj onset_adj
## 1 control 0.4946 0.0068
## 2 control 0.0869 1.9136
## 3 control 0.4716 3.0290
## 4 control 0.4712 4.5293
## 5 control 0.4701 6.0304
## 6 control 0.4700 7.5308## [,1]
## control 1
## stress -1## [,1] [,2]
## face -1.0 0.0
## object 0.5 -0.5
## place 0.5 0.5## Analysis of Variance Table
## Df Sum Sq Mean Sq F value
## group 1 2.334 2.3345 2.3345
## cond 2 59.612 29.8059 29.8059
## group:cond 2 4.068 2.0342 2.0342## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ group * cond + (1 + cond | subid)
## Data: dl_acc
##
## AIC BIC logLik deviance df.resid
## 10789.5 10877.5 -5382.8 10765.5 11268
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2853 0.2733 0.4201 0.5289 1.0637
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 0.38674 0.6219
## cond1 0.01704 0.1305 0.37
## cond2 0.04018 0.2004 -0.32 -0.75
## Number of obs: 11280, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.49874 0.09451 15.857 < 2e-16 ***
## group1 0.14208 0.09437 1.506 0.132
## cond1 -0.28942 0.04317 -6.704 2.02e-11 ***
## cond2 -0.09023 0.06788 -1.329 0.184
## group1:cond1 0.05731 0.04110 1.394 0.163
## group1:cond2 0.07734 0.06496 1.190 0.234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 cond1 cond2 grp1:1
## group1 0.028
## cond1 0.126 0.006
## cond2 -0.142 0.001 -0.162
## group1:cnd1 0.004 0.138 0.068 0.003
## group1:cnd2 0.000 -0.146 0.004 0.085 -0.176Median RT for correct responses
## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 23
## 2 stress 24## [,1]
## control 1
## stress -1## [,1] [,2]
## face -1.0 0.0
## object 0.5 -0.5
## place 0.5 0.5## Analysis of Variance Table of type III with Satterthwaite
## approximation for degrees of freedom
## Sum Sq Mean Sq NumDF DenDF F.value Pr(>F)
## group 0.002166 0.002166 1 45.000 3.784 0.05799 .
## cond 0.112059 0.056029 2 90.002 97.887 < 2e-16 ***
## group:cond 0.001425 0.000713 2 90.002 1.245 0.29290
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: respRT ~ group * cond + (1 | subid)
## Data: dl_med
##
## REML criterion at convergence: -417.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.40203 -0.52180 -0.03465 0.48143 2.08678
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 0.0107372 0.10362
## Residual 0.0005724 0.02392
## Number of obs: 141, groups: subid, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.540788 0.015252 45.000000 35.457 <2e-16 ***
## group1 0.029670 0.015252 45.000000 1.945 0.0580 .
## cond1 0.039558 0.002850 90.000000 13.880 <2e-16 ***
## cond2 0.008724 0.004936 90.000000 1.767 0.0806 .
## group1:cond1 0.003524 0.002850 90.000000 1.236 0.2195
## group1:cond2 0.004839 0.004936 90.000000 0.980 0.3296
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 cond1 cond2 grp1:1
## group1 0.021
## cond1 0.000 0.000
## cond2 0.000 0.000 0.000
## group1:cnd1 0.000 0.000 0.021 0.000
## group1:cnd2 0.000 0.000 0.000 0.021 0.000S5) Motion
Motion @retrieval (when stressor present)
dmot = read.csv('/Volumes/group/awagner/sgagnon/AP/results/group_motion.csv')
str(dmot)## 'data.frame': 71808 obs. of 13 variables:
## $ X : int 0 1 2 3 4 5 6 7 8 9 ...
## $ tr : int 0 1 2 3 4 5 6 7 8 9 ...
## $ rot_x : num -4.06e-04 -4.78e-04 -4.78e-04 -2.68e-06 5.10e-05 ...
## $ rot_y : num 0.00171 0.00192 0.00182 0.00173 0.00144 ...
## $ rot_z : num 0.000213 0.000476 0.000296 0 0.000215 ...
## $ trans_x : num 0.0538 0.0537 0.0538 0.0538 0.0538 ...
## $ trans_y : num -3.89e-05 4.74e-03 -5.50e-05 -7.32e-03 -1.96e-05 ...
## $ trans_z : num 0.000196 0.019579 0.022204 0.038783 0.044569 ...
## $ displace_abs: num 0.103 0.117 0.112 0.112 0.103 ...
## $ displace_rel: num 0 0.0269 0.0116 0.0345 0.0203 ...
## $ subid : Factor w/ 44 levels "ap100","ap101",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ run : int 1 1 1 1 1 1 1 1 1 1 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...# Retrieval TRs:
d = dmot %>% filter(!subid %in% c('ap151', 'ap156'),
run %in% c(1,2,3,4,5,6)) %>%
group_by(subid, group) %>%
summarise(max_displ = max(displace_rel),
med_displ = median(displace_rel),
n=n())
d %>% group_by(group) %>% summarise(n(), mean(max_displ), sd(max_displ),
mean(med_displ), sd(med_displ))## # A tibble: 2 x 6
## group `n()` `mean(max_displ)` `sd(max_displ)` `mean(med_displ)`
## <fctr> <int> <dbl> <dbl> <dbl>
## 1 control 22 0.4492777 0.4889915 0.03490903
## 2 stress 22 0.7557733 0.7217924 0.03705162
## # ... with 1 more variables: `sd(med_displ)` <dbl>bartlett.test(max_displ ~ group, data=d)##
## Bartlett test of homogeneity of variances
##
## data: max_displ by group
## Bartlett's K-squared = 3.0345, df = 1, p-value = 0.08151t.test(max_displ ~ group, data=d, var.equal=TRUE)##
## Two Sample t-test
##
## data: max_displ by group
## t = -1.6489, df = 42, p-value = 0.1066
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.68160841 0.06861723
## sample estimates:
## mean in group control mean in group stress
## 0.4492777 0.7557733with(d, boxplot(max_displ ~ group))
with(d, boxplot(max_displ))
bartlett.test(med_displ ~ group, data=d)##
## Bartlett test of homogeneity of variances
##
## data: med_displ by group
## Bartlett's K-squared = 2.7637, df = 1, p-value = 0.09642t.test(med_displ ~ group, data=d, var.equal=TRUE)##
## Two Sample t-test
##
## data: med_displ by group
## t = -0.66925, df = 42, p-value = 0.507
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.008603469 0.004318273
## sample estimates:
## mean in group control mean in group stress
## 0.03490903 0.03705162with(d, boxplot(med_displ ~ group))
with(d, boxplot(med_displ))
dmot %>% filter(displace_rel > 2)## X tr rot_x rot_y rot_z trans_x trans_y
## 1 32361 20 -0.005064260 -0.000988498 -0.0044876 -0.0830918 0.510269
## 2 40116 218 0.000678324 -0.002894750 -0.0113798 0.5412270 -0.192115
## trans_z displace_abs displace_rel subid run group
## 1 0.138049 0.682681 2.46140 ap121 6 control
## 2 1.835810 2.009170 3.33342 ap154 3 stressMotion @localizer
d = dmot %>% filter(!subid %in% c('ap151', 'ap156'),
run %in% c(7,8)) %>%
group_by(subid, group) %>%
summarise(max_displ = max(displace_rel),
med_displ = median(displace_rel), n=n())
d %>% group_by(group) %>% summarise(n(), mean(max_displ), mean(med_displ),
sd(max_displ), sd(med_displ))## # A tibble: 2 x 6
## group `n()` `mean(max_displ)` `mean(med_displ)` `sd(max_displ)`
## <fctr> <int> <dbl> <dbl> <dbl>
## 1 control 22 0.3324237 0.03268982 0.2681085
## 2 stress 22 0.4006318 0.03414530 0.2582950
## # ... with 1 more variables: `sd(med_displ)` <dbl>bartlett.test(max_displ ~ group, data=d)##
## Bartlett test of homogeneity of variances
##
## data: max_displ by group
## Bartlett's K-squared = 0.028515, df = 1, p-value = 0.8659t.test(max_displ ~ group, data=d, var.equal=TRUE)##
## Two Sample t-test
##
## data: max_displ by group
## t = -0.85935, df = 42, p-value = 0.395
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.22838755 0.09197127
## sample estimates:
## mean in group control mean in group stress
## 0.3324237 0.4006318bartlett.test(med_displ ~ group, data=d)##
## Bartlett test of homogeneity of variances
##
## data: med_displ by group
## Bartlett's K-squared = 0.36234, df = 1, p-value = 0.5472t.test(med_displ ~ group, data=d, var.equal=TRUE)##
## Two Sample t-test
##
## data: med_displ by group
## t = -0.54606, df = 42, p-value = 0.5879
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.006834574 0.003923610
## sample estimates:
## mean in group control mean in group stress
## 0.03268982 0.03414530S6) Other ROI Analyses
basedir = '/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/'
dt = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/mvpa/notebooks/ap_behav.csv')
head(dt)## X run trial onset duration shockCond shockTrial target associate
## 1 0 1 1 12.0191 10.6661 safe 0 BANDIT cemetery
## 2 1 1 2 22.7129 9.1242 safe 0 WALRUS foil
## 3 2 1 3 31.8642 9.4224 safe 0 VIOLIN throne_room
## 4 3 1 4 41.3132 11.3389 safe 0 MEDAL hayfield
## 5 4 1 5 52.6786 9.7644 safe 0 MANURE canyon
## 6 5 1 6 62.4701 9.1180 safe 0 SARDINE mountain_snowy
## resp acc accSpec respRT subid group remove anxious happy safe
## 1 indoor SM SMO_Hi 2.1094 ap100 control-fmri NA 3 2 3
## 2 foil CR CR 1.7773 ap100 control-fmri NA 3 2 3
## 3 indoor H HI_Hi 1.6804 ap100 control-fmri NA 3 2 3
## 4 outdoor H HO_Hi 1.7268 ap100 control-fmri NA 3 2 3
## 5 outdoor H HO_Hi 1.3734 ap100 control-fmri NA 3 2 3
## 6 outdoor H HO_Hi 2.1476 ap100 control-fmri NA 3 2 3
## stressed life_stress sleep cond_orig cond reps shock_and_post conf
## 1 3 4 4.5 TO_4 outdoor 4 0 Hi
## 2 3 4 4.5 F_0 foil 0 0 N
## 3 3 4 4.5 TI_2 indoor 2 0 Hi
## 4 3 4 4.5 TO_2 outdoor 2 0 Hi
## 5 3 4 4.5 TO_2 outdoor 2 0 Hi
## 6 3 4 4.5 TO_4 outdoor 4 0 Hi
## onset_adj mem_conditions obj_conditions onset_code rt_z
## 1 0.0191 sourcemiss_hi old itemhit -1.20388009
## 2 10.7129 CR new CR -0.12867526
## 3 19.8642 sourcehit old sourcehit -0.74601988
## 4 29.3132 sourcehit old sourcehit -0.67385952
## 5 40.6786 sourcehit old sourcehit -1.22346016
## 6 50.4701 sourcehit old sourcehit -0.01943975
## mem_conditions_byrep onset_code_byrep
## 1 sourcemiss_hi-4 itemhit-4
## 2 CR-0 CR-0
## 3 sourcehit-2 sourcehit-2
## 4 sourcehit-2 sourcehit-2
## 5 sourcehit-2 sourcehit-2
## 6 sourcehit-4 sourcehit-4counts = dt %>%
group_by(subid, mem_conditions) %>%
summarise(n=n()) %>%
ungroup() %>%
complete(subid, mem_conditions, fill = list(n=0)) %>%
filter(!mem_conditions %in% c('nuisance', 'FA', 'M'))
# length(unique(dt$mem_conditions)) * length(unique(dt$subid))
counts %>% filter(n <= 5)## # A tibble: 9 x 3
## subid mem_conditions n
## <fctr> <fctr> <dbl>
## 1 ap104 itemhit_lo 0
## 2 ap110 sourcemiss_hi 1
## 3 ap116 itemhit_lo 3
## 4 ap151 sourcehit 3
## 5 ap151 sourcemiss_hi 2
## 6 ap156 sourcehit 5
## 7 ap158 sourcemiss_hi 3
## 8 ap160 itemhit_lo 1
## 9 ap169 itemhit_lo 2Hippocampus
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character## 'data.frame': 1584 obs. of 10 variables:
## $ X : int 2 3 31 32 60 61 89 90 118 119 ...
## $ cond : Factor w/ 6 levels "CR","FA","itemhit_lo",..: 1 1 2 2 5 5 3 3 6 6 ...
## $ hemi : Factor w/ 2 levels "lh","rh": 1 2 1 2 1 2 1 2 1 2 ...
## $ mask_vox : num 69 54 69 54 69 54 69 54 69 54 ...
## $ regspace : Factor w/ 1 level "epi": 1 1 1 1 1 1 1 1 1 1 ...
## $ roi : chr "hippocampus-head" "hippocampus-head" "hippocampus-head" "hippocampus-head" ...
## $ smoothing: Factor w/ 1 level "unsmoothed": 1 1 1 1 1 1 1 1 1 1 ...
## $ subid : Factor w/ 44 levels "ap100","ap101",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : num -161 -443 -130 -458 125 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...## # A tibble: 6 x 6
## subid group cond roi value roi_num
## <fctr> <fctr> <fctr> <fctr> <dbl> <dbl>
## 1 ap100 control CR hippocampus-body -996.64670 0
## 2 ap100 control CR hippocampus-head -301.80224 -1
## 3 ap100 control CR hippocampus-tail -339.09103 1
## 4 ap100 control sourcehit hippocampus-body -97.46697 0
## 5 ap100 control sourcehit hippocampus-head 120.20124 -1
## 6 ap100 control sourcehit hippocampus-tail 361.57621 1## [,1]
## control 1
## stress -1## [,1]
## CR -1
## sourcehit 1## refitting model(s) with ML (instead of REML)## Data: data
## Models:
## object: value ~ group * cond * scale(roi_num) + (1 + cond + scale(roi_num) |
## object: subid)
## ..1: value ~ group * cond * poly(roi_num, degree = 2) + (1 + cond +
## ..1: poly(roi_num, degree = 2) | subid)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## object 15 3784.2 3837.8 -1877.1 3754.2
## ..1 23 3714.4 3796.6 -1834.2 3668.4 85.799 8 3.303e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ group * cond * poly(roi_num, degree = 2) + (1 + cond +
## poly(roi_num, degree = 2) | subid)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 3535.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7587 -0.3561 -0.0463 0.3866 4.0278
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subid (Intercept) 61011 247.0
## cond1 27983 167.3 -0.26
## poly(roi_num, degree = 2)1 4091283 2022.7 0.07 0.10
## poly(roi_num, degree = 2)2 789382 888.5 -0.18 0.20 -0.05
## Residual 18199 134.9
## Number of obs: 264, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 22.317 38.152 42.000
## group1 7.291 38.152 42.000
## cond1 213.737 26.550 42.000
## poly(roi_num, degree = 2)1 1715.127 333.441 42.000
## poly(roi_num, degree = 2)2 1537.733 190.105 42.000
## group1:cond1 39.711 26.550 42.000
## group1:poly(roi_num, degree = 2)1 808.336 333.441 42.000
## group1:poly(roi_num, degree = 2)2 -15.040 190.105 42.000
## cond1:poly(roi_num, degree = 2)1 291.992 134.905 84.220
## cond1:poly(roi_num, degree = 2)2 -240.093 134.905 84.220
## group1:cond1:poly(roi_num, degree = 2)1 290.724 134.905 84.220
## group1:cond1:poly(roi_num, degree = 2)2 -29.255 134.905 84.220
## t value Pr(>|t|)
## (Intercept) 0.585 0.5617
## group1 0.191 0.8494
## cond1 8.050 4.76e-10 ***
## poly(roi_num, degree = 2)1 5.144 6.67e-06 ***
## poly(roi_num, degree = 2)2 8.089 4.21e-10 ***
## group1:cond1 1.496 0.1422
## group1:poly(roi_num, degree = 2)1 2.424 0.0197 *
## group1:poly(roi_num, degree = 2)2 -0.079 0.9373
## cond1:poly(roi_num, degree = 2)1 2.164 0.0333 *
## cond1:poly(roi_num, degree = 2)2 -1.780 0.0787 .
## group1:cond1:poly(roi_num, degree = 2)1 2.155 0.0340 *
## group1:cond1:poly(roi_num, degree = 2)2 -0.217 0.8288
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 cond1 p(_,d=2)1 p(_,d=2)2 grp1:1 g1:(_,d=2)1
## group1 0.000
## cond1 -0.238 0.000
## ply(_,d=2)1 0.065 0.000 0.089
## ply(_,d=2)2 -0.122 0.000 0.132 -0.034
## group1:cnd1 0.000 -0.238 0.000 0.000 0.000
## g1:(_,d=2)1 0.000 0.065 0.000 0.000 0.000 0.089
## g1:(_,d=2)2 0.000 -0.122 0.000 0.000 0.000 0.132 -0.034
## c1:(_,d=2)1 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## c1:(_,d=2)2 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## g1:1:(_,d=2)1 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## g1:1:(_,d=2)2 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## g1:(_,d=2)2 c1:(_,d=2)1 c1:(_,d=2)2 g1:1:(_,d=2)1
## group1
## cond1
## ply(_,d=2)1
## ply(_,d=2)2
## group1:cnd1
## g1:(_,d=2)1
## g1:(_,d=2)2
## c1:(_,d=2)1 0.000
## c1:(_,d=2)2 0.000 0.000
## g1:1:(_,d=2)1 0.000 0.000 0.000
## g1:1:(_,d=2)2 0.000 0.000 0.000 0.000## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ group * cond + (1 | subid)
## Data: data %>% filter(roi == "hippocampus-tail")
##
## REML criterion at convergence: 1233.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.54376 -0.55680 0.06163 0.52651 2.77698
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 63192 251.4
## Residual 66186 257.3
## Number of obs: 88, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 218.52 46.78 42.00 4.671 3.07e-05 ***
## group1 67.57 46.78 42.00 1.444 0.1560
## cond1 225.30 27.42 41.15 8.215 3.27e-10 ***
## group1:cond1 60.35 27.42 41.15 2.201 0.0334 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 cond1
## group1 0.000
## cond1 0.000 0.000
## group1:cnd1 0.000 0.000 0.000## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ group * cond + (1 | subid)
## Data: data %>% filter(roi == "hippocampus-body")
##
## REML criterion at convergence: 1234.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9561 -0.5522 0.0979 0.5709 3.1301
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 43827 209.3
## Residual 78113 279.5
## Number of obs: 88, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -111.53 43.40 42.00 -2.570 0.0138 *
## group1 8.60 43.40 42.00 0.198 0.8439
## cond1 234.63 29.79 42.36 7.875 7.88e-10 ***
## group1:cond1 42.26 29.79 42.36 1.418 0.1634
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 cond1
## group1 0.000
## cond1 0.000 0.000
## group1:cnd1 0.000 0.000 0.000## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ group * cond + (1 | subid)
## Data: data %>% filter(roi == "hippocampus-head")
##
## REML criterion at convergence: 1236.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4889 -0.4274 0.0823 0.4528 3.1842
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 47527 218.0
## Residual 78198 279.6
## Number of obs: 88, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -40.04 44.37 42.00 -0.902 0.372
## group1 -54.29 44.37 42.00 -1.224 0.228
## cond1 181.28 29.81 42.01 6.081 3.03e-07 ***
## group1:cond1 16.52 29.81 42.01 0.554 0.582
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 cond1
## group1 0.000
## cond1 0.000 0.000
## group1:cnd1 0.000 0.000 0.000##
## Bartlett test of homogeneity of variances
##
## data: mean by group
## Bartlett's K-squared = 0.61163, df = 1, p-value = 0.4342##
## Two Sample t-test
##
## data: mean by group
## t = 2.4099, df = 42, p-value = 0.02042
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 41.5969 470.0802
## sample estimates:
## mean in group control mean in group stress
## 571.7385 315.9000##
## Bartlett test of homogeneity of variances
##
## data: mean by group
## Bartlett's K-squared = 1.5672, df = 1, p-value = 0.2106##
## Two Sample t-test
##
## data: mean by group
## t = 1.0997, df = 42, p-value = 0.2777
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -84.93655 288.36693
## sample estimates:
## mean in group control mean in group stress
## 173.96692 72.25173##
## Bartlett test of homogeneity of variances
##
## data: mean by group
## Bartlett's K-squared = 2.9085, df = 1, p-value = 0.08811##
## Two Sample t-test
##
## data: mean by group
## t = -0.76208, df = 42, p-value = 0.4503
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -275.5819 124.5010
## sample estimates:
## mean in group control mean in group stress
## 103.4644 179.0048
Angular
sub_remove = counts %>%
filter(mem_conditions %in% c('sourcehit', 'CR'),
n <= 5) %>%
pull(subid); sub_remove## [1] ap151 ap156
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174basedir = '/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/'
d2 = read.csv(paste(basedir,'pe_DefaultA_IPL.csv', sep=''))
data = d2 %>%
filter(cond %in% c('sourcehit', 'CR'))
# dim(data)
data = data %>% filter(!(subid %in% sub_remove))
# dim(data)
contrasts(data$group) = c(1, -1); contrasts(data$group)## [,1]
## control 1
## stress -1data$cond = factor(data$cond)
contrasts(data$cond) = c(-1,1); contrasts(data$cond)## [,1]
## CR -1
## sourcehit 1summary(lmer(value ~ group * cond + (1|subid), data=data)) # including cond RE not enough obs## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ group * cond + (1 | subid)
## Data: data
##
## REML criterion at convergence: 1397.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1244 -0.6417 -0.1722 0.7609 2.2033
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 93644 306.0
## Residual 702538 838.2
## Number of obs: 88, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -812.37 100.56 42.00 -8.079 4.34e-10 ***
## group1 -219.41 100.56 42.00 -2.182 0.0348 *
## cond1 642.48 89.35 37.87 7.191 1.39e-08 ***
## group1:cond1 98.85 89.35 37.87 1.106 0.2755
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 cond1
## group1 0.000
## cond1 0.000 0.000
## group1:cnd1 0.000 0.000 0.000
Retrosplenial
basedir = '/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/'
d2 = read.csv(paste(basedir,'pe_DefaultC_Rsp.csv', sep=''))
data = d2 %>%
filter(cond %in% c('sourcehit', 'CR')) %>%
group_by(subid, group, cond) %>%
summarise(value = mean(value))
# dim(data)
data = data %>% filter(!(subid %in% sub_remove))
# dim(data)
contrasts(data$group) = c(1, -1); contrasts(data$group)## [,1]
## control 1
## stress -1data$cond = factor(data$cond)
contrasts(data$cond) = c(-1,1); contrasts(data$cond)## [,1]
## CR -1
## sourcehit 1summary(lmer(value ~ group * cond + (1|subid), data=data)) # including cond RE not enough obs## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: value ~ group * cond + (1 | subid)
## Data: data
##
## REML criterion at convergence: 1378.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.37663 -0.46248 0.02745 0.57687 1.59530
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 246313 496.3
## Residual 431854 657.2
## Number of obs: 88, groups: subid, 44
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 215.40 102.50 42.00 2.102 0.0416 *
## group1 42.07 102.50 42.00 0.410 0.6836
## cond1 910.64 70.05 41.46 12.999 4.44e-16 ***
## group1:cond1 79.84 70.05 41.46 1.140 0.2609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) group1 cond1
## group1 0.000
## cond1 0.000 0.000
## group1:cnd1 0.000 0.000 0.000
S7) Reinstatement
VTC equalize trial counts
d = read.csv('/Users/sgagnon/Dropbox/Stanford/Presentations/AP/mvpa_logit_bilat-parahipp_fusi_inftemp_allcat_byreps_avg_46810_filtartloc_equalizetrials.csv')
dt = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/mvpa/notebooks/ap_behav.csv')
subids_rm = c('ap168', 'ap174')
with(d %>% filter(category == 'place'), table(subid, condition))## condition
## subid CR sourcehit
## ap100 60 62
## ap101 82 79
## ap102 23 21
## ap103 74 72
## ap104 75 81
## ap105 71 72
## ap107 50 50
## ap108 27 26
## ap109 38 38
## ap110 19 20
## ap111 57 59
## ap113 57 59
## ap114 49 50
## ap115 52 56
## ap116 75 72
## ap117 67 67
## ap118 36 36
## ap119 78 78
## ap120 61 62
## ap121 7 7
## ap122 34 33
## ap150 35 35
## ap152 41 40
## ap153 32 31
## ap154 42 39
## ap155 22 22
## ap157 40 40
## ap158 11 11
## ap159 50 49
## ap160 43 43
## ap161 27 27
## ap162 19 19
## ap163 74 74
## ap164 10 12
## ap165 49 53
## ap166 57 58
## ap167 32 34
## ap168 35 35
## ap169 63 64
## ap170 55 55
## ap171 32 32
## ap172 19 19
## ap173 8 8
## ap174 56 57d = d %>%
filter(!subid %in% subids_rm,
category == 'place',
condition == 'sourcehit')
dim(d)## [1] 1865 16head(d)## X index group subid run onset condition category X0
## 1 5 5 control ap100 1 29.3132 sourcehit place 5.668776
## 2 17 17 control ap100 1 137.3692 sourcehit place 2.171703
## 3 20 20 control ap100 1 147.7725 sourcehit place 3.724785
## 4 26 26 control ap100 1 189.8279 sourcehit place -2.201856
## 5 29 29 control ap100 1 199.3947 sourcehit place -6.341303
## 6 32 32 control ap100 1 249.7552 sourcehit place 4.799252
## X2 X4 X6 X8 X10 X12
## 1 7.0095601 3.2375235 -3.2345678 -1.3963429 8.6132175 10.457438
## 2 7.4833026 5.3206003 0.2201440 2.8257519 5.2779771 -1.238594
## 3 -0.3012936 -0.8840364 5.8113141 5.8149256 -3.7053775 -7.765064
## 4 -2.6881330 7.6396394 9.2119951 -1.0437411 -5.2589488 -1.989815
## 5 -1.0425030 4.3576636 4.6364212 -0.2640133 -5.1733994 -3.861562
## 6 14.6948134 8.0496648 -0.2553808 -2.2946936 0.5734839 2.898207
## avg_logit
## 1 1.804958
## 2 3.411118
## 3 1.759206
## 4 2.637236
## 5 0.889168
## 6 1.518269dj = dt %>%
mutate(onset=onset_adj, img_type=cond) %>%
dplyr::select(-group, -reps, -cond) %>%
right_join(d, by=c('subid', 'run', 'onset')) %>%
mutate(subid = factor(subid), imgType = factor(img_type))## Warning in right_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vector# str(dj)
dim(dj)## [1] 1865 46dt %>% filter(shock_and_post != 0)## X run trial onset duration shockCond shockTrial target
## 1 4308 1 38 388.8635 13.2663 threat 1 WALLET
## 2 4309 1 39 402.5077 8.7762 threat 0 WINE
## 3 4387 3 33 332.6727 13.3357 threat 1 SHRIMP
## 4 4388 3 34 346.0418 9.7581 threat 0 FAN
## 5 4443 5 5 52.4561 12.9926 threat 1 OMELET
## 6 4444 5 6 65.4843 9.5305 threat 0 SOUP
## 7 4465 5 27 280.4064 13.6152 threat 1 CAMERA
## 8 4466 5 28 294.0577 9.3696 threat 0 VINE
## 9 4580 2 16 162.8869 11.3899 threat 1 DOLL
## 10 4581 2 17 174.3062 9.9225 threat 0 ROSE
## 11 4666 4 18 188.7935 11.7510 threat 1 TOAST
## 12 4667 4 19 200.9294 8.7454 threat 0 HOOK
## 13 4753 6 21 219.7744 12.5499 threat 1 ANT
## 14 4754 6 22 232.3590 12.3240 threat 0 MONKEY
## 15 4760 6 28 294.0528 11.1433 threat 1 FENCE
## 16 4761 6 29 305.2337 9.6254 threat 0 HIGHWAY
## 17 4781 1 7 69.5282 14.3258 threat 1 LANTERN
## 18 4782 1 8 84.2717 8.9864 threat 0 BABY
## 19 4891 3 33 337.3948 11.9207 threat 1 SPONGE
## 20 4892 3 34 349.3487 9.7522 threat 0 FAN
## 21 4950 5 8 81.0813 11.1761 threat 1 RIFLE
## 22 4951 5 9 92.2900 9.9096 threat 0 CANDLE
## 23 4964 5 22 228.0153 11.6811 threat 1 PYTHON
## 24 4965 5 23 239.7295 9.0981 threat 0 SCORPION
## 25 5103 2 35 358.2914 11.2510 threat 1 ROSARY
## 26 5104 2 36 369.5712 10.5693 threat 0 SCROLL
## 27 5166 4 14 145.5132 11.6300 threat 1 QUAIL
## 28 5167 4 15 157.2813 10.4610 threat 0 TEETH
## 29 5237 6 1 12.0050 12.4959 threat 1 SPONGE
## 30 5238 6 2 24.5388 9.0512 threat 0 TROPHY
## 31 5245 6 9 93.6401 11.7989 threat 1 ORANGE
## 32 5246 6 10 105.4761 10.9878 threat 0 RUDDER
## 33 5297 1 19 194.7396 11.8971 threat 1 HOOK
## 34 5298 1 20 206.7664 11.2363 threat 0 ZIPPER
## 35 5365 3 3 30.7302 11.3891 threat 1 QUILL
## 36 5366 3 4 42.1530 9.2489 threat 0 MITTEN
## 37 5449 5 3 34.1849 11.9280 threat 1 MAP
## 38 5450 5 4 46.1465 9.6823 threat 0 KEG
## 39 5482 5 36 374.1904 12.6467 threat 1 SCISSORS
## 40 5483 5 37 386.8735 9.1087 threat 0 HAM
## 41 5610 2 38 389.7051 12.4671 threat 1 PIANO
## 42 5611 2 39 402.2028 10.9949 threat 0 RAKE
## 43 5687 4 31 315.3568 11.9062 threat 1 ASPIRIN
## 44 5688 4 32 327.2968 10.0916 threat 0 APPLE
## 45 5773 1 33 339.4238 11.0549 threat 1 PIANO
## 46 5774 1 34 350.5082 9.4189 threat 0 STAPLER
## 47 5855 3 31 316.0909 13.2648 threat 1 BIBLE
## 48 5856 3 32 329.4975 9.5201 threat 0 FAN
## 49 5922 5 14 139.8456 13.3316 threat 1 BRAIN
## 50 5923 5 15 153.2127 9.1405 threat 0 OPIUM
## 51 5924 5 16 162.3878 13.2619 threat 1 FIDDLE
## 52 5925 5 17 175.6882 12.3158 threat 0 HIGHWAY
## 53 6062 2 28 288.3530 12.5493 threat 1 GARBAGE
## 54 6063 2 29 301.2897 11.3239 threat 0 FORK
## 55 6143 4 25 254.2745 14.3201 threat 1 RABBIT
## 56 6144 4 26 268.6304 10.1910 threat 0 WINE
## 57 6211 6 9 93.2720 12.7042 threat 1 CLAM
## 58 6212 6 10 106.0143 9.8862 threat 0 EASEL
## 59 6240 6 38 388.5212 12.0980 threat 1 TOAST
## 60 6241 6 39 400.6577 10.1893 threat 0 SCORPION
## 61 6311 2 25 263.0002 12.1957 threat 1 QUAIL
## 62 6312 2 26 275.2265 9.3894 threat 0 TWEEZERS
## 63 6405 4 35 361.1516 13.6753 threat 1 SARDINE
## 64 6406 4 36 374.8610 9.9290 threat 0 GEESE
## 65 6474 6 20 208.5525 12.3666 threat 1 PEARL
## 66 6475 6 21 220.9555 9.8877 threat 0 JUGGLER
## 67 6492 6 38 395.3022 11.3682 threat 1 POSTER
## 68 6493 6 39 406.7067 9.1399 threat 0 ROSARY
## 69 6515 1 19 197.5353 13.2642 threat 1 MANURE
## 70 6516 1 20 210.8293 9.3761 threat 0 GOAT
## 71 6536 1 40 413.6314 11.0594 threat 1 NET
## 72 6537 1 41 424.8264 10.3646 threat 0 CORAL
## 73 6596 3 16 168.0995 11.6279 threat 1 MONKEY
## 74 6597 3 17 179.7602 9.9870 threat 0 ROSE
## 75 6676 5 12 129.1745 12.1020 threat 1 RACQUET
## 76 6677 5 13 141.3117 11.5306 threat 1 WHIP
## 77 6678 5 14 152.8769 9.9073 threat 0 PUDDLE
## 78 6809 2 19 195.2545 14.3221 threat 1 TWEEZERS
## 79 6810 2 20 209.6069 9.7612 threat 0 GEESE
## 80 6901 4 27 275.6425 11.6810 threat 1 CARROT
## 81 6902 4 28 287.4599 12.2185 threat 0 ANT
## 82 6966 6 8 83.6481 11.4126 threat 1 NOODLE
## 83 6967 6 9 95.0966 10.9899 threat 0 LETTUCE
## 84 6972 6 14 143.4211 11.9064 threat 1 SHEEP
## 85 6973 6 15 155.3631 10.1932 threat 0 LION
## 86 7032 1 32 329.2043 11.4189 threat 1 SPATULA
## 87 7033 1 33 340.7563 9.1498 threat 0 ORCHID
## 88 7115 3 31 316.7444 11.1134 threat 1 BOAT
## 89 7116 3 32 327.8903 12.3220 threat 0 SAXOPHONE
## 90 7172 5 4 42.1248 11.9183 threat 1 HARPOON
## 91 7173 5 5 54.0802 9.1177 threat 0 PYTHON
## 92 7199 5 31 318.8659 11.1393 threat 1 LANTERN
## 93 7200 5 32 330.0435 9.0496 threat 0 ICICLE
## 94 7311 2 17 176.4960 13.3340 threat 1 DOVE
## 95 7312 2 18 189.8603 9.1013 threat 0 SARDINE
## 96 7402 4 24 252.1435 12.6475 threat 1 SLIPPER
## 97 7403 4 25 264.9348 9.8093 threat 0 PLUG
## 98 7466 6 4 43.3746 12.5480 threat 1 PIANO
## 99 7467 6 5 55.9605 9.6796 threat 0 BARREL
## 100 7492 6 30 316.6024 12.1877 threat 1 RACQUET
## 101 7493 6 31 328.8293 9.1158 threat 0 TROPHY
## 102 7550 2 4 43.1248 12.0943 threat 1 WALLET
## 103 7551 2 5 55.2492 9.8056 threat 0 BOAT
## 104 7636 4 6 60.3592 11.9181 threat 1 TURTLE
## 105 7637 4 7 72.6665 9.0304 threat 0 CARROT
## 106 7732 6 18 183.3171 13.1799 threat 1 BARREL
## 107 7733 6 19 196.5336 10.9880 threat 0 CLOAK
## 108 7743 6 29 300.6094 11.7544 threat 1 BENCH
## 109 7744 6 30 312.4025 10.4626 threat 0 GEESE
## 110 7784 1 28 286.1872 11.6897 threat 1 ANT
## 111 7785 1 29 297.9053 9.0583 threat 0 TRIBE
## 112 7850 3 10 100.1384 11.0979 threat 1 WORM
## 113 7851 3 11 111.6157 9.2843 threat 0 KILT
## 114 7934 5 10 106.2110 12.7053 threat 1 WHEAT
## 115 7935 5 11 118.9534 9.1085 threat 0 ENVELOPE
## 116 7965 5 41 420.2549 14.3217 threat 1 SALAD
## 117 7966 5 42 434.6141 9.7532 threat 0 RICE
## 118 8059 2 9 92.6728 11.6839 threat 1 STOMACH
## 119 8060 2 10 104.3876 10.0960 threat 0 WALLET
## 120 8141 4 7 75.6126 12.3677 threat 1 NEEDLE
## 121 8142 4 8 88.0141 10.1931 threat 0 PUMPKIN
## 122 8236 6 18 192.0202 13.1773 threat 1 BOWL
## 123 8237 6 19 205.2341 9.2689 threat 0 PEARL
## 124 8247 6 29 303.3809 12.1005 threat 1 TROPHY
## 125 8248 6 30 315.5210 9.0480 threat 0 HAM
## 126 9566 2 4 44.0804 12.6507 threat 1 KEG
## 127 9567 2 5 56.7611 12.3272 threat 0 LION
## 128 9658 4 12 125.3795 13.3322 threat 1 CHOCOLATE
## 129 9659 4 13 138.8557 9.8073 threat 0 WHEAT
## 130 9743 6 13 131.1672 12.1920 threat 1 RASH
## 131 9744 6 14 143.3945 9.3837 threat 0 BABY
## 132 9768 6 38 389.1359 13.9377 threat 1 CAMEL
## 133 9769 6 39 403.1104 9.5289 threat 0 DAISY
## 134 9792 1 20 206.5058 11.1158 threat 1 DOLL
## 135 9793 1 21 217.7754 9.8078 threat 0 NET
## 136 9887 3 31 316.4073 12.6500 threat 1 PYTHON
## 137 9888 3 32 329.0896 9.0559 threat 0 PUMPKIN
## 138 9964 5 24 250.0028 11.9268 threat 1 ELEPHANT
## 139 9965 5 25 261.9648 10.1008 threat 0 RUDDER
## 140 9972 5 32 331.3766 11.7487 threat 1 FIRE
## 141 9973 5 33 343.1625 9.4118 threat 0 TULIP
## 142 10064 1 40 409.8509 11.1258 threat 1 FORK
## 143 10065 1 41 421.1248 9.2718 threat 0 PUDDLE
## 144 10120 3 12 125.2129 11.3880 threat 1 PASSPORT
## 145 10121 3 13 136.6359 11.1801 threat 0 FOX
## 146 10225 5 33 337.9285 11.9068 threat 1 APPLE
## 147 10226 5 34 349.8706 11.6155 threat 0 NECKLACE
## 148 10232 5 40 411.8589 11.8892 threat 1 CHOCOLATE
## 149 10233 5 41 423.7828 9.3700 threat 0 VINE
## 150 10334 2 16 163.6714 11.8030 threat 1 UMBRELLA
## 151 10335 2 17 175.5047 10.6519 threat 0 STOMACH
## 152 10412 4 10 103.6759 12.2727 threat 1 WALNUT
## 153 10413 4 11 115.9812 9.4173 threat 0 DOVE
## 154 10510 6 24 243.0234 12.1000 threat 1 CHOCOLATE
## 155 10511 6 25 255.1586 10.6464 threat 0 MUSTARD
## 156 10525 6 39 401.7446 11.9819 threat 1 TYPEWRITER
## 157 10526 6 40 413.7626 9.7478 threat 0 BONE
## 158 10608 2 38 386.7055 11.1138 threat 1 PLUG
## 159 10609 2 39 397.8499 9.6884 threat 0 VIOLIN
## 160 10659 4 5 55.3607 12.7068 threat 1 SHRIMP
## 161 10660 4 6 68.2091 10.0868 threat 0 SALAD
## 162 10743 6 5 55.3874 11.3702 threat 1 BLANKET
## 163 10744 6 6 66.7930 9.4131 threat 0 BRAIN
## 164 10762 6 24 247.0549 12.5635 threat 1 EMPEROR
## 165 10763 6 25 259.6565 9.5264 threat 0 RACQUET
## 166 11035 1 3 32.9018 11.9231 threat 1 ARMOR
## 167 11036 1 4 44.9635 10.0881 threat 0 GLOBE
## 168 11145 3 29 298.7042 11.6854 threat 1 SCORPION
## 169 11146 3 30 310.4258 11.6751 threat 0 ACCORDIAN
## 170 11217 5 17 175.5205 11.0963 threat 1 LAMP
## 171 11218 5 18 186.6540 9.2675 threat 0 OATMEAL
## 172 11220 5 20 206.0835 13.6732 threat 1 WALRUS
## 173 11221 5 21 219.7938 10.5466 threat 0 FROG
## 174 11287 1 3 30.6409 11.9242 threat 1 BANANA
## 175 11288 1 4 42.5929 11.6821 threat 0 SYRUP
## 176 11395 3 27 280.7332 14.3224 threat 1 SHARK
## 177 11396 3 28 295.2130 9.2906 threat 0 SOUP
## 178 11465 5 13 136.9597 11.7581 threat 1 RUDDER
## 179 11466 5 14 148.7535 9.7489 threat 0 ASPARAGUS
## 180 11490 5 38 392.5558 12.7040 threat 1 SUNBURN
## 181 11491 5 39 405.2980 9.1074 threat 0 LIGHTNING
## 182 11575 1 39 400.3330 12.9952 threat 1 KNIFE
## 183 11576 1 40 413.4640 9.8156 threat 0 OPIUM
## 184 11657 3 37 379.6906 12.6483 threat 1 QUAIL
## 185 11658 3 38 392.3713 9.6865 threat 0 PAN
## 186 11714 5 10 104.7707 11.1194 threat 1 EAGLE
## 187 11715 5 11 115.9277 11.6733 threat 0 SHARK
## 188 11721 5 17 180.2945 12.5609 threat 1 LANTERN
## 189 11722 5 18 192.8931 11.6123 threat 0 SANDAL
## associate resp acc accSpec respRT subid
## 1 castle outdoor H HO_Hi 3.4784 ap150
## 2 foil indoor FA FAI_Lo 1.5662 ap150
## 3 bowling_alley indoor H HI_Hi 1.9713 ap150
## 4 heliport outdoor H HO_Hi 2.0455 ap150
## 5 bookstore indoor H HI_Lo 1.2885 ap150
## 6 barn outdoor H HO_Lo 1.5677 ap150
## 7 catacomb indoor H HI_Hi 1.9171 ap150
## 8 butchers_shop indoor H HI_Lo 2.1234 ap150
## 9 foil no response no response no response 999.0000 ap151
## 10 foil no response no response no response 999.0000 ap151
## 11 control_room outdoor SM SMI_Lo 2.6113 ap151
## 12 cockpit outdoor SM SMI_Lo 1.8112 ap151
## 13 inn indoor SM SMO_Lo 0.0420 ap151
## 14 landing_deck indoor SM SMO_Lo 1.4202 ap151
## 15 bayou foil M MI 2.1244 ap151
## 16 foil outdoor FA FAO_Lo 2.3634 ap151
## 17 hospital_room outdoor SM SMI_Lo 2.2153 ap152
## 18 foil foil CR CR 1.5186 ap152
## 19 bus_interior outdoor SM SMI_Lo 1.7358 ap152
## 20 boat_deck outdoor H HO_Lo 1.4701 ap152
## 21 foil no response no response no response 999.0000 ap152
## 22 chicken_coop outdoor SM SMI_Lo 1.7785 ap152
## 23 amusement_park outdoor H HO_Lo 4.0942 ap152
## 24 butte outdoor H HO_Hi 1.4965 ap152
## 25 foil no response no response no response 999.0000 ap153
## 26 brewery indoor H HI_Lo 3.4916 ap153
## 27 baseball_field indoor SM SMO_Lo 1.9538 ap153
## 28 heliport outdoor H HO_Lo 2.5545 ap153
## 29 rice_paddy outdoor H HO_Lo 1.7886 ap153
## 30 foil foil CR CR 2.7072 ap153
## 31 cottage_garden no response no response no response 999.0000 ap153
## 32 excavation outdoor H HO_Lo 2.5612 ap153
## 33 carrousel outdoor H HO_Lo 2.5473 ap154
## 34 harbor indoor SM SMO_Lo 2.4948 ap154
## 35 picnic_area outdoor H HO_Lo 2.2851 ap154
## 36 foil foil CR CR 2.3313 ap154
## 37 field_cultivated no response no response no response 999.0000 ap154
## 38 foil indoor FA FAI_Lo 2.7874 ap154
## 39 foil indoor FA FAI_Lo 2.0466 ap154
## 40 foil foil CR CR 1.0724 ap154
## 41 banquet_hall indoor H HI_Hi 0.0624 ap155
## 42 cubicle outdoor SM SMI_Hi 3.0532 ap155
## 43 moat indoor SM SMO_Hi 0.0764 ap155
## 44 nursery outdoor SM SMI_Hi 2.1297 ap155
## 45 ice_skating_rink no response no response no response 999.0000 ap156
## 46 art_studio indoor H HI_Lo 2.0028 ap156
## 47 drugstore foil M MI 1.7543 ap156
## 48 foil indoor FA FAI_Lo 2.3347 ap156
## 49 foil no response no response no response 999.0000 ap156
## 50 nursery indoor H HI_Hi 2.1255 ap156
## 51 boxing_ring indoor H HI_Lo 1.4665 ap156
## 52 butte outdoor H HO_Lo 1.5602 ap156
## 53 boardwalk indoor SM SMO_Hi 2.7003 ap157
## 54 biology_laboratory outdoor SM SMI_Lo 2.2663 ap157
## 55 foil indoor FA FAI_Lo 2.5947 ap157
## 56 throne_room outdoor SM SMI_Lo 3.2820 ap157
## 57 sauna foil M MI 2.3324 ap157
## 58 foil foil CR CR 1.9194 ap157
## 59 foil foil CR CR 2.4756 ap157
## 60 squash_court indoor H HI_Lo 3.7308 ap157
## 61 foil no response no response no response 999.0000 ap159
## 62 foil foil CR CR 1.7951 ap159
## 63 foil no response no response no response 999.0000 ap159
## 64 car_interior foil M MI 2.8011 ap159
## 65 foil foil CR CR 0.0110 ap159
## 66 foil foil CR CR 2.2897 ap159
## 67 hotel_room indoor H HI_Lo 0.5815 ap159
## 68 foil foil CR CR 1.5919 ap159
## 69 foil no response no response no response 999.0000 ap160
## 70 foil foil CR CR 0.8235 ap160
## 71 foil foil CR CR 1.6742 ap160
## 72 beach indoor no response no response 999.0000 ap160
## 73 restaurant_kitchen no response no response no response 999.0000 ap160
## 74 game_room indoor H HI_Hi 1.3730 ap160
## 75 aqueduct no response no response no response 999.0000 ap160
## 76 ocean outdoor H HO_Hi 0.1927 ap160
## 77 bazaar foil M MI 1.4969 ap160
## 78 foil no response no response no response 999.0000 ap161
## 79 archive foil M MI 1.1672 ap161
## 80 hotel_room outdoor SM SMI_Lo 2.0545 ap161
## 81 auto_factory outdoor SM SMI_Lo 2.8225 ap161
## 82 harbor no response no response no response 999.0000 ap161
## 83 palace indoor SM SMO_Lo 2.6534 ap161
## 84 foil indoor FA FAI_Lo 0.0530 ap161
## 85 foil indoor FA FAI_Hi 2.4873 ap161
## 86 forest_needleleaf indoor SM SMO_Lo 3.4906 ap162
## 87 foil indoor FA FAI_Hi 2.0639 ap162
## 88 greenhouse indoor SM SMO_Lo 0.0078 ap162
## 89 cloister indoor H HI_Hi 3.7735 ap162
## 90 foil foil CR CR 1.8254 ap162
## 91 parlor indoor H HI_Hi 2.8492 ap162
## 92 foil foil CR CR 2.8869 ap162
## 93 bar outdoor SM SMI_Hi 1.9891 ap162
## 94 diner no response no response no response 999.0000 ap163
## 95 mountain_snowy outdoor H HO_Hi 1.3257 ap163
## 96 parking_garage outdoor SM SMI_Hi 3.6060 ap163
## 97 foil foil CR CR 1.5522 ap163
## 98 foil foil CR CR 2.7758 ap163
## 99 galley indoor H HI_Hi 2.6550 ap163
## 100 foil no response no response no response 999.0000 ap163
## 101 elevator outdoor SM SMI_Hi 3.8893 ap163
## 102 outhouse no response no response no response 999.0000 ap165
## 103 foil outdoor FA FAO_Hi 2.2418 ap165
## 104 library indoor H HI_Hi 3.0487 ap165
## 105 playground outdoor H HO_Lo 2.5955 ap165
## 106 art_studio no response no response no response 999.0000 ap165
## 107 casino indoor H HI_Hi 1.6615 ap165
## 108 foil outdoor FA FAO_Hi 1.5531 ap165
## 109 foil foil CR CR 1.8396 ap165
## 110 foil no response no response no response 999.0000 ap166
## 111 jewelry_shop indoor H HI_Hi 1.3814 ap166
## 112 apse indoor H HI_Lo 1.8952 ap166
## 113 cloister outdoor SM SMI_Hi 3.3552 ap166
## 114 foil foil CR CR 1.4887 ap166
## 115 foil foil CR CR 1.6873 ap166
## 116 aquarium no response no response no response 999.0000 ap166
## 117 toyshop outdoor SM SMI_Hi 1.7564 ap166
## 118 construction_site no response no response no response 999.0000 ap167
## 119 foil outdoor FA FAO_Hi 2.1556 ap167
## 120 hot_tub outdoor H HO_Lo 0.1760 ap167
## 121 foil outdoor FA FAO_Hi 2.3297 ap167
## 122 foil foil CR CR 0.1413 ap167
## 123 desert foil M MI 2.2095 ap167
## 124 foil foil CR CR 1.1858 ap167
## 125 lighthouse foil M MI 1.0927 ap167
## 126 throne_room no response no response no response 999.0000 ap169
## 127 courtroom foil M MI 1.9232 ap169
## 128 watering_hole outdoor H HO_Hi 2.3657 ap169
## 129 childs_room outdoor SM SMI_Hi 2.8110 ap169
## 130 aquarium no response no response no response 999.0000 ap169
## 131 putting_green foil M MI 2.8250 ap169
## 132 foil no response no response no response 999.0000 ap169
## 133 harbor foil M MI 1.8486 ap169
## 134 toyshop foil M MI 1.2386 ap170
## 135 crosswalk outdoor H HO_Hi 2.4052 ap170
## 136 phone_booth outdoor H HO_Hi 0.8689 ap170
## 137 kennel outdoor SM SMI_Lo 2.4475 ap170
## 138 auto_factory outdoor SM SMI_Hi 0.7318 ap170
## 139 chicken_coop outdoor SM SMI_Lo 3.6921 ap170
## 140 foil foil CR CR 2.5005 ap170
## 141 foil foil CR CR 2.5760 ap170
## 142 foil outdoor FA FAO_Lo 1.7813 ap172
## 143 foil indoor FA FAI_Lo 1.2331 ap172
## 144 cliff outdoor H HO_Hi 2.6850 ap172
## 145 foil outdoor FA FAO_Lo 2.2064 ap172
## 146 computer_room no response no response no response 999.0000 ap172
## 147 balcony indoor SM SMO_Lo 2.0357 ap172
## 148 corral no response no response no response 999.0000 ap172
## 149 ball_pit indoor H HI_Lo 1.5934 ap172
## 150 art_studio indoor H HI_Lo 0.0960 ap173
## 151 foil foil CR CR 1.6203 ap173
## 152 labyrinth foil M MI 0.0595 ap173
## 153 foil outdoor FA FAO_Lo 1.6019 ap173
## 154 foil foil CR CR 2.2311 ap173
## 155 ice_floe foil M MI 0.5755 ap173
## 156 stage no response no response no response 999.0000 ap173
## 157 foil foil CR CR 0.6016 ap173
## 158 foil no response no response no response 999.0000 ap171
## 159 bookstore indoor H HI_Hi 2.3229 ap171
## 160 ruin outdoor H HO_Lo 1.8824 ap171
## 161 jewelry_shop outdoor SM SMI_Lo 1.8924 ap171
## 162 patio no response no response no response 999.0000 ap171
## 163 cockpit outdoor SM SMI_Lo 1.8604 ap171
## 164 foil foil CR CR 1.1141 ap171
## 165 lock_chamber foil M MI 1.4302 ap171
## 166 beauty_salon outdoor SM SMI_Lo 1.1529 ap168
## 167 swimming_pool outdoor SM SMI_Hi 1.4554 ap168
## 168 rock_arch outdoor H HO_Hi 1.9075 ap168
## 169 general_store indoor SM SMO_Lo 2.7764 ap168
## 170 music_studio outdoor SM SMI_Hi 2.7333 ap168
## 171 lido_deck foil M MI 1.2860 ap168
## 172 foil foil CR CR 0.4739 ap168
## 173 outhouse outdoor H HO_Lo 2.0904 ap168
## 174 jewelry_shop no response no response no response 999.0000 ap164
## 175 kennel indoor H HI_Lo 2.5060 ap164
## 176 subway_interior indoor H HI_Hi 2.6877 ap164
## 177 bowling_alley indoor H HI_Lo 2.1773 ap164
## 178 amusement_arcade no response no response no response 999.0000 ap164
## 179 foil indoor FA FAI_Lo 3.7373 ap164
## 180 foil outdoor FA FAO_Lo 2.1194 ap164
## 181 watering_hole indoor SM SMO_Lo 1.4898 ap164
## 182 foil indoor FA FAI_Lo 2.4353 ap174
## 183 foil foil CR CR 1.3682 ap174
## 184 art_studio outdoor SM SMI_Hi 0.2030 ap174
## 185 formal_garden indoor SM SMO_Lo 3.2013 ap174
## 186 fishpond outdoor H HO_Lo 2.5289 ap174
## 187 diner indoor H HI_Hi 1.5974 ap174
## 188 subway_interior indoor H HI_Lo 0.1130 ap174
## 189 foil outdoor FA FAO_Hi 2.5575 ap174
## group remove anxious happy safe stressed life_stress sleep
## 1 stress-fmri NA 4 2 4 5 3 7.5
## 2 stress-fmri NA 4 2 4 5 3 7.5
## 3 stress-fmri NA 4 2 4 5 3 7.5
## 4 stress-fmri NA 4 2 4 5 3 7.5
## 5 stress-fmri NA 4 2 4 5 3 7.5
## 6 stress-fmri NA 4 2 4 5 3 7.5
## 7 stress-fmri NA 4 2 4 5 3 7.5
## 8 stress-fmri NA 4 2 4 5 3 7.5
## 9 stress-fmri NA 6 1 3 5 4 9.0
## 10 stress-fmri NA 6 1 3 5 4 9.0
## 11 stress-fmri NA 6 1 3 5 4 9.0
## 12 stress-fmri NA 6 1 3 5 4 9.0
## 13 stress-fmri NA 6 1 3 5 4 9.0
## 14 stress-fmri NA 6 1 3 5 4 9.0
## 15 stress-fmri NA 6 1 3 5 4 9.0
## 16 stress-fmri NA 6 1 3 5 4 9.0
## 17 stress-fmri NA 3 4 5 6 3 9.0
## 18 stress-fmri NA 3 4 5 6 3 9.0
## 19 stress-fmri NA 3 4 5 6 3 9.0
## 20 stress-fmri NA 3 4 5 6 3 9.0
## 21 stress-fmri NA 3 4 5 6 3 9.0
## 22 stress-fmri NA 3 4 5 6 3 9.0
## 23 stress-fmri NA 3 4 5 6 3 9.0
## 24 stress-fmri NA 3 4 5 6 3 9.0
## 25 stress-fmri NA 2 5 5 2 4 7.0
## 26 stress-fmri NA 2 5 5 2 4 7.0
## 27 stress-fmri NA 2 5 5 2 4 7.0
## 28 stress-fmri NA 2 5 5 2 4 7.0
## 29 stress-fmri NA 2 5 5 2 4 7.0
## 30 stress-fmri NA 2 5 5 2 4 7.0
## 31 stress-fmri NA 2 5 5 2 4 7.0
## 32 stress-fmri NA 2 5 5 2 4 7.0
## 33 stress-fmri NA 5 4 4 5 4 8.0
## 34 stress-fmri NA 5 4 4 5 4 8.0
## 35 stress-fmri NA 5 4 4 5 4 8.0
## 36 stress-fmri NA 5 4 4 5 4 8.0
## 37 stress-fmri NA 5 4 4 5 4 8.0
## 38 stress-fmri NA 5 4 4 5 4 8.0
## 39 stress-fmri NA 5 4 4 5 4 8.0
## 40 stress-fmri NA 5 4 4 5 4 8.0
## 41 stress-fmri NA 3 5 6 2 2 8.0
## 42 stress-fmri NA 3 5 6 2 2 8.0
## 43 stress-fmri NA 3 5 6 2 2 8.0
## 44 stress-fmri NA 3 5 6 2 2 8.0
## 45 stress-fmri NA 3 2 7 3 2 9.0
## 46 stress-fmri NA 3 2 7 3 2 9.0
## 47 stress-fmri NA 3 2 7 3 2 9.0
## 48 stress-fmri NA 3 2 7 3 2 9.0
## 49 stress-fmri NA 3 2 7 3 2 9.0
## 50 stress-fmri NA 3 2 7 3 2 9.0
## 51 stress-fmri NA 3 2 7 3 2 9.0
## 52 stress-fmri NA 3 2 7 3 2 9.0
## 53 stress-fmri NA 3 2 5 3 3 8.0
## 54 stress-fmri NA 3 2 5 3 3 8.0
## 55 stress-fmri NA 3 2 5 3 3 8.0
## 56 stress-fmri NA 3 2 5 3 3 8.0
## 57 stress-fmri NA 3 2 5 3 3 8.0
## 58 stress-fmri NA 3 2 5 3 3 8.0
## 59 stress-fmri NA 3 2 5 3 3 8.0
## 60 stress-fmri NA 3 2 5 3 3 8.0
## 61 stress-fmri NA 5 3 5 4 6 12.0
## 62 stress-fmri NA 5 3 5 4 6 12.0
## 63 stress-fmri NA 5 3 5 4 6 12.0
## 64 stress-fmri NA 5 3 5 4 6 12.0
## 65 stress-fmri NA 5 3 5 4 6 12.0
## 66 stress-fmri NA 5 3 5 4 6 12.0
## 67 stress-fmri NA 5 3 5 4 6 12.0
## 68 stress-fmri NA 5 3 5 4 6 12.0
## 69 stress-fmri NA 4 3 6 5 3 4.5
## 70 stress-fmri NA 4 3 6 5 3 4.5
## 71 stress-fmri NA 4 3 6 5 3 4.5
## 72 stress-fmri NA 4 3 6 5 3 4.5
## 73 stress-fmri NA 4 3 6 5 3 4.5
## 74 stress-fmri NA 4 3 6 5 3 4.5
## 75 stress-fmri NA 4 3 6 5 3 4.5
## 76 stress-fmri NA 4 3 6 5 3 4.5
## 77 stress-fmri NA 4 3 6 5 3 4.5
## 78 stress-fmri NA 3 3 7 6 5 6.0
## 79 stress-fmri NA 3 3 7 6 5 6.0
## 80 stress-fmri NA 3 3 7 6 5 6.0
## 81 stress-fmri NA 3 3 7 6 5 6.0
## 82 stress-fmri NA 3 3 7 6 5 6.0
## 83 stress-fmri NA 3 3 7 6 5 6.0
## 84 stress-fmri NA 3 3 7 6 5 6.0
## 85 stress-fmri NA 3 3 7 6 5 6.0
## 86 stress-fmri NA 3 2 4 5 3 7.0
## 87 stress-fmri NA 3 2 4 5 3 7.0
## 88 stress-fmri NA 3 2 4 5 3 7.0
## 89 stress-fmri NA 3 2 4 5 3 7.0
## 90 stress-fmri NA 3 2 4 5 3 7.0
## 91 stress-fmri NA 3 2 4 5 3 7.0
## 92 stress-fmri NA 3 2 4 5 3 7.0
## 93 stress-fmri NA 3 2 4 5 3 7.0
## 94 stress-fmri NA 3 2 4 3 4 8.0
## 95 stress-fmri NA 3 2 4 3 4 8.0
## 96 stress-fmri NA 3 2 4 3 4 8.0
## 97 stress-fmri NA 3 2 4 3 4 8.0
## 98 stress-fmri NA 3 2 4 3 4 8.0
## 99 stress-fmri NA 3 2 4 3 4 8.0
## 100 stress-fmri NA 3 2 4 3 4 8.0
## 101 stress-fmri NA 3 2 4 3 4 8.0
## 102 stress-fmri NA 5 2 5 5 2 7.5
## 103 stress-fmri NA 5 2 5 5 2 7.5
## 104 stress-fmri NA 5 2 5 5 2 7.5
## 105 stress-fmri NA 5 2 5 5 2 7.5
## 106 stress-fmri NA 5 2 5 5 2 7.5
## 107 stress-fmri NA 5 2 5 5 2 7.5
## 108 stress-fmri NA 5 2 5 5 2 7.5
## 109 stress-fmri NA 5 2 5 5 2 7.5
## 110 stress-fmri NA 5 2 5 4 4 6.0
## 111 stress-fmri NA 5 2 5 4 4 6.0
## 112 stress-fmri NA 5 2 5 4 4 6.0
## 113 stress-fmri NA 5 2 5 4 4 6.0
## 114 stress-fmri NA 5 2 5 4 4 6.0
## 115 stress-fmri NA 5 2 5 4 4 6.0
## 116 stress-fmri NA 5 2 5 4 4 6.0
## 117 stress-fmri NA 5 2 5 4 4 6.0
## 118 stress-fmri NA 6 2 3 6 3 9.0
## 119 stress-fmri NA 6 2 3 6 3 9.0
## 120 stress-fmri NA 6 2 3 6 3 9.0
## 121 stress-fmri NA 6 2 3 6 3 9.0
## 122 stress-fmri NA 6 2 3 6 3 9.0
## 123 stress-fmri NA 6 2 3 6 3 9.0
## 124 stress-fmri NA 6 2 3 6 3 9.0
## 125 stress-fmri NA 6 2 3 6 3 9.0
## 126 stress-fmri NA 5 2 5 4 4 7.5
## 127 stress-fmri NA 5 2 5 4 4 7.5
## 128 stress-fmri NA 5 2 5 4 4 7.5
## 129 stress-fmri NA 5 2 5 4 4 7.5
## 130 stress-fmri NA 5 2 5 4 4 7.5
## 131 stress-fmri NA 5 2 5 4 4 7.5
## 132 stress-fmri NA 5 2 5 4 4 7.5
## 133 stress-fmri NA 5 2 5 4 4 7.5
## 134 stress-fmri NA 2 1 7 3 5 6.0
## 135 stress-fmri NA 2 1 7 3 5 6.0
## 136 stress-fmri NA 2 1 7 3 5 6.0
## 137 stress-fmri NA 2 1 7 3 5 6.0
## 138 stress-fmri NA 2 1 7 3 5 6.0
## 139 stress-fmri NA 2 1 7 3 5 6.0
## 140 stress-fmri NA 2 1 7 3 5 6.0
## 141 stress-fmri NA 2 1 7 3 5 6.0
## 142 stress-fmri NA 4 2 4 5 6 6.0
## 143 stress-fmri NA 4 2 4 5 6 6.0
## 144 stress-fmri NA 4 2 4 5 6 6.0
## 145 stress-fmri NA 4 2 4 5 6 6.0
## 146 stress-fmri NA 4 2 4 5 6 6.0
## 147 stress-fmri NA 4 2 4 5 6 6.0
## 148 stress-fmri NA 4 2 4 5 6 6.0
## 149 stress-fmri NA 4 2 4 5 6 6.0
## 150 stress-fmri NA 6 2 3 5 4 9.5
## 151 stress-fmri NA 6 2 3 5 4 9.5
## 152 stress-fmri NA 6 2 3 5 4 9.5
## 153 stress-fmri NA 6 2 3 5 4 9.5
## 154 stress-fmri NA 6 2 3 5 4 9.5
## 155 stress-fmri NA 6 2 3 5 4 9.5
## 156 stress-fmri NA 6 2 3 5 4 9.5
## 157 stress-fmri NA 6 2 3 5 4 9.5
## 158 stress-fmri NA 3 3 3 4 3 7.5
## 159 stress-fmri NA 3 3 3 4 3 7.5
## 160 stress-fmri NA 3 3 3 4 3 7.5
## 161 stress-fmri NA 3 3 3 4 3 7.5
## 162 stress-fmri NA 3 3 3 4 3 7.5
## 163 stress-fmri NA 3 3 3 4 3 7.5
## 164 stress-fmri NA 3 3 3 4 3 7.5
## 165 stress-fmri NA 3 3 3 4 3 7.5
## 166 stress-fmri NA 7 2 5 7 6 8.0
## 167 stress-fmri NA 7 2 5 7 6 8.0
## 168 stress-fmri NA 7 2 5 7 6 8.0
## 169 stress-fmri NA 7 2 5 7 6 8.0
## 170 stress-fmri NA 7 2 5 7 6 8.0
## 171 stress-fmri NA 7 2 5 7 6 8.0
## 172 stress-fmri NA 7 2 5 7 6 8.0
## 173 stress-fmri NA 7 2 5 7 6 8.0
## 174 stress-fmri NA 6 2 3 6 5 9.0
## 175 stress-fmri NA 6 2 3 6 5 9.0
## 176 stress-fmri NA 6 2 3 6 5 9.0
## 177 stress-fmri NA 6 2 3 6 5 9.0
## 178 stress-fmri NA 6 2 3 6 5 9.0
## 179 stress-fmri NA 6 2 3 6 5 9.0
## 180 stress-fmri NA 6 2 3 6 5 9.0
## 181 stress-fmri NA 6 2 3 6 5 9.0
## 182 stress-fmri NA 2 6 7 2 3 9.0
## 183 stress-fmri NA 2 6 7 2 3 9.0
## 184 stress-fmri NA 2 6 7 2 3 9.0
## 185 stress-fmri NA 2 6 7 2 3 9.0
## 186 stress-fmri NA 2 6 7 2 3 9.0
## 187 stress-fmri NA 2 6 7 2 3 9.0
## 188 stress-fmri NA 2 6 7 2 3 9.0
## 189 stress-fmri NA 2 6 7 2 3 9.0
## cond_orig cond reps shock_and_post conf onset_adj mem_conditions
## 1 TO_2 outdoor 2 1 Hi 376.8635 nuisance
## 2 F_0 foil 0 1 Lo 390.5077 nuisance
## 3 TI_4 indoor 4 1 Hi 320.6727 nuisance
## 4 TO_2 outdoor 2 1 Hi 334.0418 nuisance
## 5 TI_2 indoor 2 1 Lo 40.4561 nuisance
## 6 TO_4 outdoor 4 1 Lo 53.4843 nuisance
## 7 TI_4 indoor 4 1 Hi 268.4064 nuisance
## 8 TI_4 indoor 4 1 Lo 282.0577 nuisance
## 9 F_0 foil 0 1 N 150.8869 nuisance
## 10 F_0 foil 0 1 N 162.3062 nuisance
## 11 TI_4 indoor 4 1 Lo 176.7935 nuisance
## 12 TI_4 indoor 4 1 Lo 188.9294 nuisance
## 13 TO_2 outdoor 2 1 Lo 207.7744 nuisance
## 14 TO_4 outdoor 4 1 Lo 220.3590 nuisance
## 15 TO_2 outdoor 2 1 N 282.0528 nuisance
## 16 F_0 foil 0 1 Lo 293.2337 nuisance
## 17 TI_2 indoor 2 1 Lo 57.5282 nuisance
## 18 F_0 foil 0 1 N 72.2717 nuisance
## 19 TI_2 indoor 2 1 Lo 325.3948 nuisance
## 20 TO_2 outdoor 2 1 Lo 337.3487 nuisance
## 21 F_0 foil 0 1 N 69.0813 nuisance
## 22 TI_4 indoor 4 1 Lo 80.2900 nuisance
## 23 TO_4 outdoor 4 1 Lo 216.0153 nuisance
## 24 TO_2 outdoor 2 1 Hi 227.7295 nuisance
## 25 F_0 foil 0 1 N 346.2914 nuisance
## 26 TI_2 indoor 2 1 Lo 357.5712 nuisance
## 27 TO_4 outdoor 4 1 Lo 133.5132 nuisance
## 28 TO_2 outdoor 2 1 Lo 145.2813 nuisance
## 29 TO_2 outdoor 2 1 Lo 0.0050 nuisance
## 30 F_0 foil 0 1 N 12.5388 nuisance
## 31 TO_2 outdoor 2 1 N 81.6401 nuisance
## 32 TO_4 outdoor 4 1 Lo 93.4761 nuisance
## 33 TO_4 outdoor 4 1 Lo 182.7396 nuisance
## 34 TO_2 outdoor 2 1 Lo 194.7664 nuisance
## 35 TO_2 outdoor 2 1 Lo 18.7302 nuisance
## 36 F_0 foil 0 1 N 30.1530 nuisance
## 37 TO_4 outdoor 4 1 N 22.1849 nuisance
## 38 F_0 foil 0 1 Lo 34.1465 nuisance
## 39 F_0 foil 0 1 Lo 362.1904 nuisance
## 40 F_0 foil 0 1 N 374.8735 nuisance
## 41 TI_4 indoor 4 1 Hi 377.7051 nuisance
## 42 TI_4 indoor 4 1 Hi 390.2028 nuisance
## 43 TO_2 outdoor 2 1 Hi 303.3568 nuisance
## 44 TI_4 indoor 4 1 Hi 315.2968 nuisance
## 45 TI_2 indoor 2 1 N 327.4238 nuisance
## 46 TI_4 indoor 4 1 Lo 338.5082 nuisance
## 47 TI_2 indoor 2 1 N 304.0909 nuisance
## 48 F_0 foil 0 1 Lo 317.4975 nuisance
## 49 F_0 foil 0 1 N 127.8456 nuisance
## 50 TI_4 indoor 4 1 Hi 141.2127 nuisance
## 51 TI_4 indoor 4 1 Lo 150.3878 nuisance
## 52 TO_4 outdoor 4 1 Lo 163.6882 nuisance
## 53 TO_4 outdoor 4 1 Hi 276.3530 nuisance
## 54 TI_2 indoor 2 1 Lo 289.2897 nuisance
## 55 F_0 foil 0 1 Lo 242.2745 nuisance
## 56 TI_2 indoor 2 1 Lo 256.6304 nuisance
## 57 TI_2 indoor 2 1 N 81.2720 nuisance
## 58 F_0 foil 0 1 N 94.0143 nuisance
## 59 F_0 foil 0 1 N 376.5212 nuisance
## 60 TI_4 indoor 4 1 Lo 388.6577 nuisance
## 61 F_0 foil 0 1 N 251.0002 nuisance
## 62 F_0 foil 0 1 N 263.2265 nuisance
## 63 F_0 foil 0 1 N 349.1516 nuisance
## 64 TI_2 indoor 2 1 N 362.8610 nuisance
## 65 F_0 foil 0 1 N 196.5525 nuisance
## 66 F_0 foil 0 1 N 208.9555 nuisance
## 67 TI_2 indoor 2 1 Lo 383.3022 nuisance
## 68 F_0 foil 0 1 N 394.7067 nuisance
## 69 F_0 foil 0 1 N 185.5353 nuisance
## 70 F_0 foil 0 1 N 198.8293 nuisance
## 71 F_0 foil 0 1 N 401.6314 nuisance
## 72 TO_2 outdoor 2 1 N 412.8264 nuisance
## 73 TI_2 indoor 2 1 N 156.0995 nuisance
## 74 TI_4 indoor 4 1 Hi 167.7602 nuisance
## 75 TO_2 outdoor 2 1 N 117.1745 nuisance
## 76 TO_4 outdoor 4 2 Hi 129.3117 nuisance
## 77 TI_2 indoor 2 1 N 140.8769 nuisance
## 78 F_0 foil 0 1 N 183.2545 nuisance
## 79 TI_2 indoor 2 1 N 197.6069 nuisance
## 80 TI_4 indoor 4 1 Lo 263.6425 nuisance
## 81 TI_2 indoor 2 1 Lo 275.4599 nuisance
## 82 TO_4 outdoor 4 1 N 71.6481 nuisance
## 83 TO_4 outdoor 4 1 Lo 83.0966 nuisance
## 84 F_0 foil 0 1 Lo 131.4211 nuisance
## 85 F_0 foil 0 1 Hi 143.3631 nuisance
## 86 TO_2 outdoor 2 1 Lo 317.2043 nuisance
## 87 F_0 foil 0 1 Hi 328.7563 nuisance
## 88 TO_2 outdoor 2 1 Lo 304.7444 nuisance
## 89 TI_2 indoor 2 1 Hi 315.8903 nuisance
## 90 F_0 foil 0 1 N 30.1248 nuisance
## 91 TI_4 indoor 4 1 Hi 42.0802 nuisance
## 92 F_0 foil 0 1 N 306.8659 nuisance
## 93 TI_2 indoor 2 1 Hi 318.0435 nuisance
## 94 TI_4 indoor 4 1 N 164.4960 nuisance
## 95 TO_4 outdoor 4 1 Hi 177.8603 nuisance
## 96 TI_4 indoor 4 1 Hi 240.1435 nuisance
## 97 F_0 foil 0 1 N 252.9348 nuisance
## 98 F_0 foil 0 1 N 31.3746 nuisance
## 99 TI_4 indoor 4 1 Hi 43.9605 nuisance
## 100 F_0 foil 0 1 N 304.6024 nuisance
## 101 TI_2 indoor 2 1 Hi 316.8293 nuisance
## 102 TO_2 outdoor 2 1 N 31.1248 nuisance
## 103 F_0 foil 0 1 Hi 43.2492 nuisance
## 104 TI_2 indoor 2 1 Hi 48.3592 nuisance
## 105 TO_4 outdoor 4 1 Lo 60.6665 nuisance
## 106 TI_4 indoor 4 1 N 171.3171 nuisance
## 107 TI_2 indoor 2 1 Hi 184.5336 nuisance
## 108 F_0 foil 0 1 Hi 288.6094 nuisance
## 109 F_0 foil 0 1 N 300.4025 nuisance
## 110 F_0 foil 0 1 N 274.1872 nuisance
## 111 TI_4 indoor 4 1 Hi 285.9053 nuisance
## 112 TI_4 indoor 4 1 Lo 88.1384 nuisance
## 113 TI_4 indoor 4 1 Hi 99.6157 nuisance
## 114 F_0 foil 0 1 N 94.2110 nuisance
## 115 F_0 foil 0 1 N 106.9534 nuisance
## 116 TI_2 indoor 2 1 N 408.2549 nuisance
## 117 TI_4 indoor 4 1 Hi 422.6141 nuisance
## 118 TO_4 outdoor 4 1 N 80.6728 nuisance
## 119 F_0 foil 0 1 Hi 92.3876 nuisance
## 120 TO_2 outdoor 2 1 Lo 63.6126 nuisance
## 121 F_0 foil 0 1 Hi 76.0141 nuisance
## 122 F_0 foil 0 1 N 180.0202 nuisance
## 123 TO_4 outdoor 4 1 N 193.2341 nuisance
## 124 F_0 foil 0 1 N 291.3809 nuisance
## 125 TO_2 outdoor 2 1 N 303.5210 nuisance
## 126 TI_4 indoor 4 1 N 32.0804 nuisance
## 127 TI_2 indoor 2 1 N 44.7611 nuisance
## 128 TO_2 outdoor 2 1 Hi 113.3795 nuisance
## 129 TI_4 indoor 4 1 Hi 126.8557 nuisance
## 130 TI_2 indoor 2 1 N 119.1672 nuisance
## 131 TO_2 outdoor 2 1 N 131.3945 nuisance
## 132 F_0 foil 0 1 N 377.1359 nuisance
## 133 TO_4 outdoor 4 1 N 391.1104 nuisance
## 134 TI_2 indoor 2 1 N 194.5058 nuisance
## 135 TO_4 outdoor 4 1 Hi 205.7754 nuisance
## 136 TO_4 outdoor 4 1 Hi 304.4073 nuisance
## 137 TI_4 indoor 4 1 Lo 317.0896 nuisance
## 138 TI_2 indoor 2 1 Hi 238.0028 nuisance
## 139 TI_2 indoor 2 1 Lo 249.9648 nuisance
## 140 F_0 foil 0 1 N 319.3766 nuisance
## 141 F_0 foil 0 1 N 331.1625 nuisance
## 142 F_0 foil 0 1 Lo 397.8509 nuisance
## 143 F_0 foil 0 1 Lo 409.1248 nuisance
## 144 TO_4 outdoor 4 1 Hi 113.2129 nuisance
## 145 F_0 foil 0 1 Lo 124.6359 nuisance
## 146 TI_4 indoor 4 1 N 325.9285 nuisance
## 147 TO_4 outdoor 4 1 Lo 337.8706 nuisance
## 148 TO_4 outdoor 4 1 N 399.8589 nuisance
## 149 TI_2 indoor 2 1 Lo 411.7828 nuisance
## 150 TI_2 indoor 2 1 Lo 151.6714 nuisance
## 151 F_0 foil 0 1 N 163.5047 nuisance
## 152 TO_2 outdoor 2 1 N 91.6759 nuisance
## 153 F_0 foil 0 1 Lo 103.9812 nuisance
## 154 F_0 foil 0 1 N 231.0234 nuisance
## 155 TO_2 outdoor 2 1 N 243.1586 nuisance
## 156 TI_2 indoor 2 1 N 389.7446 nuisance
## 157 F_0 foil 0 1 N 401.7626 nuisance
## 158 F_0 foil 0 1 N 374.7055 nuisance
## 159 TI_4 indoor 4 1 Hi 385.8499 nuisance
## 160 TO_4 outdoor 4 1 Lo 43.3607 nuisance
## 161 TI_2 indoor 2 1 Lo 56.2091 nuisance
## 162 TO_2 outdoor 2 1 N 43.3874 nuisance
## 163 TI_2 indoor 2 1 Lo 54.7930 nuisance
## 164 F_0 foil 0 1 N 235.0549 nuisance
## 165 TO_4 outdoor 4 1 N 247.6565 nuisance
## 166 TI_2 indoor 2 1 Lo 20.9018 nuisance
## 167 TI_2 indoor 2 1 Hi 32.9635 nuisance
## 168 TO_2 outdoor 2 1 Hi 286.7042 nuisance
## 169 TO_2 outdoor 2 1 Lo 298.4258 nuisance
## 170 TI_2 indoor 2 1 Hi 163.5205 nuisance
## 171 TO_4 outdoor 4 1 N 174.6540 nuisance
## 172 F_0 foil 0 1 N 194.0835 nuisance
## 173 TO_4 outdoor 4 1 Lo 207.7938 nuisance
## 174 TI_2 indoor 2 1 N 18.6409 nuisance
## 175 TI_4 indoor 4 1 Lo 30.5929 nuisance
## 176 TI_4 indoor 4 1 Hi 268.7332 nuisance
## 177 TI_2 indoor 2 1 Lo 283.2130 nuisance
## 178 TI_4 indoor 4 1 N 124.9597 nuisance
## 179 F_0 foil 0 1 Lo 136.7535 nuisance
## 180 F_0 foil 0 1 Lo 380.5558 nuisance
## 181 TO_4 outdoor 4 1 Lo 393.2980 nuisance
## 182 F_0 foil 0 1 Lo 388.3330 nuisance
## 183 F_0 foil 0 1 N 401.4640 nuisance
## 184 TI_4 indoor 4 1 Hi 367.6906 nuisance
## 185 TO_2 outdoor 2 1 Lo 380.3713 nuisance
## 186 TO_2 outdoor 2 1 Lo 92.7707 nuisance
## 187 TI_4 indoor 4 1 Hi 103.9277 nuisance
## 188 TI_2 indoor 2 1 Lo 168.2945 nuisance
## 189 F_0 foil 0 1 Hi 180.8931 nuisance
## obj_conditions onset_code rt_z mem_conditions_byrep
## 1 nuisance nuisance -1.02609880 nuisance-2
## 2 nuisance nuisance -1.02982661 nuisance-0
## 3 nuisance nuisance -1.02903687 nuisance-4
## 4 nuisance nuisance -1.02889222 nuisance-2
## 5 nuisance nuisance -1.03036798 nuisance-2
## 6 nuisance nuisance -1.02982369 nuisance-4
## 7 nuisance nuisance -1.02914254 nuisance-4
## 8 nuisance nuisance -1.02874036 nuisance-4
## 9 nuisance nuisance 1.16189396 nuisance-0
## 10 nuisance nuisance 1.16189396 nuisance-0
## 11 nuisance nuisance -0.77288225 nuisance-4
## 12 nuisance nuisance -0.77443587 nuisance-4
## 13 nuisance nuisance -0.77787129 nuisance-2
## 14 nuisance nuisance -0.77519511 nuisance-4
## 15 nuisance nuisance -0.77382771 nuisance-2
## 16 nuisance nuisance -0.77336362 nuisance-0
## 17 nuisance nuisance -0.35306673 nuisance-2
## 18 nuisance nuisance -0.35504331 nuisance-0
## 19 nuisance nuisance -0.35442710 nuisance-2
## 20 nuisance nuisance -0.35518090 nuisance-2
## 21 nuisance nuisance 2.47486619 nuisance-0
## 22 nuisance nuisance -0.35430596 nuisance-4
## 23 nuisance nuisance -0.34773619 nuisance-4
## 24 nuisance nuisance -0.35510601 nuisance-2
## 25 nuisance nuisance 1.16189456 nuisance-0
## 26 nuisance nuisance -0.77268781 nuisance-2
## 27 nuisance nuisance -0.77567624 nuisance-4
## 28 nuisance nuisance -0.77450889 nuisance-2
## 29 nuisance nuisance -0.77599727 nuisance-2
## 30 nuisance nuisance -0.77421215 nuisance-0
## 31 nuisance nuisance 1.16189456 nuisance-2
## 32 nuisance nuisance -0.77449587 nuisance-4
## 33 nuisance nuisance -0.96299790 nuisance-4
## 34 nuisance nuisance -0.96309941 nuisance-2
## 35 nuisance nuisance -0.96350486 nuisance-2
## 36 nuisance nuisance -0.96341553 nuisance-0
## 37 nuisance nuisance 0.96362385 nuisance-4
## 38 nuisance nuisance -0.96253367 nuisance-0
## 39 nuisance nuisance -0.96396600 nuisance-0
## 40 nuisance nuisance -0.96584959 nuisance-0
## 41 nuisance nuisance -0.45005369 nuisance-4
## 42 nuisance nuisance -0.44335047 nuisance-4
## 43 nuisance nuisance -0.45002231 nuisance-2
## 44 nuisance nuisance -0.44542029 nuisance-4
## 45 nuisance nuisance 0.88949910 nuisance-2
## 46 nuisance nuisance -1.03750001 nuisance-4
## 47 nuisance nuisance -1.03798031 nuisance-2
## 48 nuisance nuisance -1.03685851 nuisance-0
## 49 nuisance nuisance 0.88949910 nuisance-0
## 50 nuisance nuisance -1.03726285 nuisance-4
## 51 nuisance nuisance -1.03853657 nuisance-4
## 52 nuisance nuisance -1.03835546 nuisance-4
## 53 nuisance nuisance 0.06444887 nuisance-4
## 54 nuisance nuisance -0.67920844 nuisance-2
## 55 nuisance nuisance -0.11649632 nuisance-0
## 56 nuisance nuisance 1.06118956 nuisance-2
## 57 nuisance nuisance -0.56594635 nuisance-2
## 58 nuisance nuisance -1.27362024 nuisance-0
## 59 nuisance nuisance -0.32057370 nuisance-0
## 60 nuisance nuisance 1.83020662 nuisance-4
## 61 nuisance nuisance 1.16189367 nuisance-0
## 62 nuisance nuisance -0.77404563 nuisance-0
## 63 nuisance nuisance 1.16189367 nuisance-0
## 64 nuisance nuisance -0.77209262 nuisance-2
## 65 nuisance nuisance -0.77750922 nuisance-0
## 66 nuisance nuisance -0.77308543 nuisance-0
## 67 nuisance nuisance -0.77640167 nuisance-2
## 68 nuisance nuisance -0.77444012 nuisance-0
## 69 nuisance nuisance 0.31333973 nuisance-0
## 70 nuisance nuisance -3.13439400 nuisance-0
## 71 nuisance nuisance -3.13145565 nuisance-0
## 72 nuisance nuisance 0.31333973 nuisance-2
## 73 nuisance nuisance 0.31333973 nuisance-2
## 74 nuisance nuisance -3.13249601 nuisance-4
## 75 nuisance nuisance 0.31333973 nuisance-2
## 76 nuisance nuisance -3.13657280 nuisance-4
## 77 nuisance nuisance -3.13206805 nuisance-2
## 78 nuisance nuisance 1.04446483 nuisance-0
## 79 nuisance nuisance -0.87174272 nuisance-2
## 80 nuisance nuisance -0.87003878 nuisance-4
## 81 nuisance nuisance -0.86856394 nuisance-2
## 82 nuisance nuisance 1.04446483 nuisance-4
## 83 nuisance nuisance -0.86888867 nuisance-4
## 84 nuisance nuisance -0.87388240 nuisance-0
## 85 nuisance nuisance -0.86920765 nuisance-0
## 86 nuisance nuisance -0.47165160 nuisance-2
## 87 nuisance nuisance -0.47504671 nuisance-0
## 88 nuisance nuisance -0.47993961 nuisance-2
## 89 nuisance nuisance -0.47097838 nuisance-2
## 90 nuisance nuisance -0.47561427 nuisance-0
## 91 nuisance nuisance -0.47317794 nuisance-4
## 92 nuisance nuisance -0.47308822 nuisance-0
## 93 nuisance nuisance -0.47522472 nuisance-2
## 94 nuisance nuisance 1.04446489 nuisance-4
## 95 nuisance nuisance -0.87290175 nuisance-4
## 96 nuisance nuisance -0.86851938 nuisance-4
## 97 nuisance nuisance -0.87246645 nuisance-0
## 98 nuisance nuisance -0.87011489 nuisance-0
## 99 nuisance nuisance -0.87034705 nuisance-4
## 100 nuisance nuisance 1.04446489 nuisance-0
## 101 nuisance nuisance -0.86797493 nuisance-2
## 102 nuisance nuisance 1.62018425 nuisance-2
## 103 nuisance nuisance -0.53987700 nuisance-0
## 104 nuisance nuisance -0.53812837 nuisance-2
## 105 nuisance nuisance -0.53911050 nuisance-4
## 106 nuisance nuisance 1.62018425 nuisance-4
## 107 nuisance nuisance -0.54113456 nuisance-2
## 108 nuisance nuisance -0.54136947 nuisance-0
## 109 nuisance nuisance -0.54074860 nuisance-0
## 110 nuisance nuisance 1.16189437 nuisance-0
## 111 nuisance nuisance -0.77565661 nuisance-4
## 112 nuisance nuisance -0.77465872 nuisance-4
## 113 nuisance nuisance -0.77182314 nuisance-4
## 114 nuisance nuisance -0.77544821 nuisance-0
## 115 nuisance nuisance -0.77506250 nuisance-0
## 116 nuisance nuisance 1.16189437 nuisance-2
## 117 nuisance nuisance -0.77492829 nuisance-4
## 118 nuisance nuisance 1.03774819 nuisance-4
## 119 nuisance nuisance -0.88789825 nuisance-0
## 120 nuisance nuisance -0.89172233 nuisance-2
## 121 nuisance nuisance -0.88756193 nuisance-0
## 122 nuisance nuisance -0.89178936 nuisance-0
## 123 nuisance nuisance -0.88779413 nuisance-4
## 124 nuisance nuisance -0.88977165 nuisance-0
## 125 nuisance nuisance -0.88995150 nuisance-2
## 126 nuisance nuisance 1.05409235 nuisance-4
## 127 nuisance nuisance -0.84409535 nuisance-2
## 128 nuisance nuisance -0.84325293 nuisance-2
## 129 nuisance nuisance -0.84240519 nuisance-4
## 130 nuisance nuisance 1.05409235 nuisance-2
## 131 nuisance nuisance -0.84237854 nuisance-2
## 132 nuisance nuisance 1.05409235 nuisance-0
## 133 nuisance nuisance -0.84423737 nuisance-4
## 134 nuisance nuisance -0.47628889 nuisance-2
## 135 nuisance nuisance -0.47351358 nuisance-4
## 136 nuisance nuisance -0.47716840 nuisance-4
## 137 nuisance nuisance -0.47341295 nuisance-4
## 138 nuisance nuisance -0.47749456 nuisance-2
## 139 nuisance nuisance -0.47045207 nuisance-2
## 140 nuisance nuisance -0.47328686 nuisance-0
## 141 nuisance nuisance -0.47310725 nuisance-0
## 142 nuisance nuisance -0.77487066 nuisance-0
## 143 nuisance nuisance -0.77593536 nuisance-0
## 144 nuisance nuisance -0.77311553 nuisance-4
## 145 nuisance nuisance -0.77404505 nuisance-0
## 146 nuisance nuisance 1.16189469 nuisance-4
## 147 nuisance nuisance -0.77437658 nuisance-4
## 148 nuisance nuisance 1.16189469 nuisance-4
## 149 nuisance nuisance -0.77523560 nuisance-2
## 150 nuisance nuisance -1.57307784 nuisance-2
## 151 nuisance nuisance -1.56974499 nuisance-0
## 152 nuisance nuisance -1.57315765 nuisance-2
## 153 nuisance nuisance -1.56978522 nuisance-0
## 154 nuisance nuisance -1.56840948 nuisance-0
## 155 nuisance nuisance -1.57202942 nuisance-2
## 156 nuisance nuisance 0.61100983 nuisance-2
## 157 nuisance nuisance -1.57197236 nuisance-0
## 158 nuisance nuisance 1.62018469 nuisance-0
## 159 nuisance nuisance -0.53882141 nuisance-4
## 160 nuisance nuisance -0.53977563 nuisance-4
## 161 nuisance nuisance -0.53975396 nuisance-2
## 162 nuisance nuisance 1.62018469 nuisance-2
## 163 nuisance nuisance -0.53982328 nuisance-2
## 164 nuisance nuisance -0.54143992 nuisance-0
## 165 nuisance nuisance -0.54075518 nuisance-4
## 166 nuisance nuisance -0.33508200 nuisance-2
## 167 nuisance nuisance -0.33417201 nuisance-2
## 168 nuisance nuisance -0.33281200 nuisance-2
## 169 nuisance nuisance -0.33019816 nuisance-2
## 170 nuisance nuisance -0.33032781 nuisance-2
## 171 nuisance nuisance -0.33468161 nuisance-4
## 172 nuisance nuisance -0.33712458 nuisance-0
## 173 nuisance nuisance -0.33226180 nuisance-4
## 174 nuisance nuisance 1.33333239 nuisance-2
## 175 nuisance nuisance -0.66655966 nuisance-4
## 176 nuisance nuisance -0.66619500 nuisance-4
## 177 nuisance nuisance -0.66721934 nuisance-2
## 178 nuisance nuisance 1.33333239 nuisance-4
## 179 nuisance nuisance -0.66408853 nuisance-0
## 180 nuisance nuisance -0.66733554 nuisance-0
## 181 nuisance nuisance -0.66859910 nuisance-4
## 182 nuisance nuisance -0.33127179 nuisance-0
## 183 nuisance nuisance -0.33448191 nuisance-0
## 184 nuisance nuisance -0.33798713 nuisance-4
## 185 nuisance nuisance -0.32896747 nuisance-2
## 186 nuisance nuisance -0.33099022 nuisance-2
## 187 nuisance nuisance -0.33379241 nuisance-4
## 188 nuisance nuisance -0.33825787 nuisance-2
## 189 nuisance nuisance -0.33090418 nuisance-0
## onset_code_byrep
## 1 nuisance-2
## 2 nuisance-0
## 3 nuisance-4
## 4 nuisance-2
## 5 nuisance-2
## 6 nuisance-4
## 7 nuisance-4
## 8 nuisance-4
## 9 nuisance-0
## 10 nuisance-0
## 11 nuisance-4
## 12 nuisance-4
## 13 nuisance-2
## 14 nuisance-4
## 15 nuisance-2
## 16 nuisance-0
## 17 nuisance-2
## 18 nuisance-0
## 19 nuisance-2
## 20 nuisance-2
## 21 nuisance-0
## 22 nuisance-4
## 23 nuisance-4
## 24 nuisance-2
## 25 nuisance-0
## 26 nuisance-2
## 27 nuisance-4
## 28 nuisance-2
## 29 nuisance-2
## 30 nuisance-0
## 31 nuisance-2
## 32 nuisance-4
## 33 nuisance-4
## 34 nuisance-2
## 35 nuisance-2
## 36 nuisance-0
## 37 nuisance-4
## 38 nuisance-0
## 39 nuisance-0
## 40 nuisance-0
## 41 nuisance-4
## 42 nuisance-4
## 43 nuisance-2
## 44 nuisance-4
## 45 nuisance-2
## 46 nuisance-4
## 47 nuisance-2
## 48 nuisance-0
## 49 nuisance-0
## 50 nuisance-4
## 51 nuisance-4
## 52 nuisance-4
## 53 nuisance-4
## 54 nuisance-2
## 55 nuisance-0
## 56 nuisance-2
## 57 nuisance-2
## 58 nuisance-0
## 59 nuisance-0
## 60 nuisance-4
## 61 nuisance-0
## 62 nuisance-0
## 63 nuisance-0
## 64 nuisance-2
## 65 nuisance-0
## 66 nuisance-0
## 67 nuisance-2
## 68 nuisance-0
## 69 nuisance-0
## 70 nuisance-0
## 71 nuisance-0
## 72 nuisance-2
## 73 nuisance-2
## 74 nuisance-4
## 75 nuisance-2
## 76 nuisance-4
## 77 nuisance-2
## 78 nuisance-0
## 79 nuisance-2
## 80 nuisance-4
## 81 nuisance-2
## 82 nuisance-4
## 83 nuisance-4
## 84 nuisance-0
## 85 nuisance-0
## 86 nuisance-2
## 87 nuisance-0
## 88 nuisance-2
## 89 nuisance-2
## 90 nuisance-0
## 91 nuisance-4
## 92 nuisance-0
## 93 nuisance-2
## 94 nuisance-4
## 95 nuisance-4
## 96 nuisance-4
## 97 nuisance-0
## 98 nuisance-0
## 99 nuisance-4
## 100 nuisance-0
## 101 nuisance-2
## 102 nuisance-2
## 103 nuisance-0
## 104 nuisance-2
## 105 nuisance-4
## 106 nuisance-4
## 107 nuisance-2
## 108 nuisance-0
## 109 nuisance-0
## 110 nuisance-0
## 111 nuisance-4
## 112 nuisance-4
## 113 nuisance-4
## 114 nuisance-0
## 115 nuisance-0
## 116 nuisance-2
## 117 nuisance-4
## 118 nuisance-4
## 119 nuisance-0
## 120 nuisance-2
## 121 nuisance-0
## 122 nuisance-0
## 123 nuisance-4
## 124 nuisance-0
## 125 nuisance-2
## 126 nuisance-4
## 127 nuisance-2
## 128 nuisance-2
## 129 nuisance-4
## 130 nuisance-2
## 131 nuisance-2
## 132 nuisance-0
## 133 nuisance-4
## 134 nuisance-2
## 135 nuisance-4
## 136 nuisance-4
## 137 nuisance-4
## 138 nuisance-2
## 139 nuisance-2
## 140 nuisance-0
## 141 nuisance-0
## 142 nuisance-0
## 143 nuisance-0
## 144 nuisance-4
## 145 nuisance-0
## 146 nuisance-4
## 147 nuisance-4
## 148 nuisance-4
## 149 nuisance-2
## 150 nuisance-2
## 151 nuisance-0
## 152 nuisance-2
## 153 nuisance-0
## 154 nuisance-0
## 155 nuisance-2
## 156 nuisance-2
## 157 nuisance-0
## 158 nuisance-0
## 159 nuisance-4
## 160 nuisance-4
## 161 nuisance-2
## 162 nuisance-2
## 163 nuisance-2
## 164 nuisance-0
## 165 nuisance-4
## 166 nuisance-2
## 167 nuisance-2
## 168 nuisance-2
## 169 nuisance-2
## 170 nuisance-2
## 171 nuisance-4
## 172 nuisance-0
## 173 nuisance-4
## 174 nuisance-2
## 175 nuisance-4
## 176 nuisance-4
## 177 nuisance-2
## 178 nuisance-4
## 179 nuisance-0
## 180 nuisance-0
## 181 nuisance-4
## 182 nuisance-0
## 183 nuisance-0
## 184 nuisance-4
## 185 nuisance-2
## 186 nuisance-2
## 187 nuisance-4
## 188 nuisance-2
## 189 nuisance-0with(dt %>% filter(shock_and_post != 0), table(cond, mem_conditions))## mem_conditions
## cond CR FA itemhit_lo M nuisance sourcehit sourcemiss_hi
## foil 0 0 0 0 63 0 0
## indoor 0 0 0 0 71 0 0
## outdoor 0 0 0 0 55 0 0dj %>% filter(shock_and_post != 0)## [1] X.x run trial
## [4] onset duration shockCond
## [7] shockTrial target associate
## [10] resp acc accSpec
## [13] respRT subid remove
## [16] anxious happy safe
## [19] stressed life_stress sleep
## [22] cond_orig shock_and_post conf
## [25] onset_adj mem_conditions obj_conditions
## [28] onset_code rt_z mem_conditions_byrep
## [31] onset_code_byrep img_type X.y
## [34] index group condition
## [37] category X0 X2
## [40] X4 X6 X8
## [43] X10 X12 avg_logit
## [46] imgType
## <0 rows> (or 0-length row.names)dj = dj %>% filter(shock_and_post == 0)
dim(dj)## [1] 1865 46subids_lowtrials = dj %>%
group_by(group, subid) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), fill = list(count= 0)) %>%
filter(count < 6) %>%
pull(subid) %>% unique()
subids_lowtrials## factor(0)
## 42 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ... ap173contrasts(dj$group) = c(1,-1); contrasts(dj$group)## [,1]
## control 1
## stress -1with(dj, table(subid))## subid
## ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ap110 ap111 ap113
## 62 79 21 72 81 72 50 26 38 20 59 59
## ap114 ap115 ap116 ap117 ap118 ap119 ap120 ap121 ap122 ap150 ap152 ap153
## 50 56 72 67 36 78 62 7 33 35 40 31
## ap154 ap155 ap157 ap158 ap159 ap160 ap161 ap162 ap163 ap164 ap165 ap166
## 39 22 40 11 49 43 27 19 74 12 53 58
## ap167 ap169 ap170 ap171 ap172 ap173
## 34 64 55 32 19 8subids_rm## [1] "ap168" "ap174"d_avg = dj %>% filter(!subid %in% subids_lowtrials) %>%
group_by(group, subid) %>%
summarise(mean=mean(avg_logit))
bartlett.test(mean ~ group, data=d_avg)##
## Bartlett test of homogeneity of variances
##
## data: mean by group
## Bartlett's K-squared = 2.6769, df = 1, p-value = 0.1018t.test(mean ~ group, data=d_avg, var.equal=TRUE)##
## Two Sample t-test
##
## data: mean by group
## t = 1.5914, df = 40, p-value = 0.1194
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.09784085 0.82253259
## sample estimates:
## mean in group control mean in group stress
## 1.146385 0.784039d_avg %>% group_by(group) %>% summarise(mean(mean), n())## # A tibble: 2 x 3
## group `mean(mean)` `n()`
## <fctr> <dbl> <int>
## 1 control 1.146385 22
## 2 stress 0.784039 20boxplot(mean ~ group, data=d_avg)
ggplot(d_avg, aes(x=group, y=mean, color=group)) +
geom_boxplot() + geom_jitter() +
scale_color_manual(values = palette) +
xlab('') + ylab('Logit')
t.test(d_avg %>% filter(group == 'control') %>% pull(mean), mu = 0)##
## One Sample t-test
##
## data: d_avg %>% filter(group == "control") %>% pull(mean)
## t = 9.0135, df = 21, p-value = 1.156e-08
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.8818883 1.4108815
## sample estimates:
## mean of x
## 1.146385t.test(d_avg %>% filter(group == 'stress') %>% pull(mean), mu = 0)##
## One Sample t-test
##
## data: d_avg %>% filter(group == "stress") %>% pull(mean)
## t = 4.0485, df = 19, p-value = 0.0006859
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.3786987 1.1893793
## sample estimates:
## mean of x
## 0.784039S8) Inferior parietal reinstatement
Load in data
d = read.csv('/Users/sgagnon/Dropbox/Stanford/Presentations/AP/mvpa_logit_place_byreps_avg_46810_filtartloc_scalewithinrun.csv')
subids_rm = c('ap168') # bad localizer classification
# filter d to just good subjs, and only OLD items
d = d %>%
filter(!subid %in% subids_rm) %>%
filter(cond %in% c('sourcemiss_hi', 'sourcehit', 'M', 'itemhit_lo')) %>%
mutate(pcorr = factor(ifelse(cond == "sourcehit", 1, 0)),
vtc_logit = avg_logit)
dt %>% filter(shock_and_post == 2) # one subj w/consecutive shocks - trial needs to be removed (though mem_condition == 'nuisance')## X run trial onset duration shockCond shockTrial target associate
## 1 6677 5 13 141.3117 11.5306 threat 1 WHIP ocean
## resp acc accSpec respRT subid group remove anxious happy safe
## 1 outdoor H HO_Hi 0.1927 ap160 stress-fmri NA 4 3 6
## stressed life_stress sleep cond_orig cond reps shock_and_post conf
## 1 5 3 4.5 TO_4 outdoor 4 2 Hi
## onset_adj mem_conditions obj_conditions onset_code rt_z
## 1 129.3117 nuisance nuisance nuisance -3.136573
## mem_conditions_byrep onset_code_byrep
## 1 nuisance-4 nuisance-4# Merge w/other behavioral info
d = dt %>%
mutate(onset=onset_adj, img_type=cond) %>%
dplyr::select(-group, -reps, -cond) %>%
right_join(d, by=c('subid', 'run', 'onset')) %>%
mutate(subid = factor(subid), imgType = factor(img_type))## Warning in right_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vectordim(d)## [1] 6781 50d = d %>% filter(shock_and_post == 0)
dim(d)## [1] 6780 50# Read in hipp BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-hippocampus.csv')
roi_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-hippocampus.csv')
# dim(roi_lh); dim(roi_rh)
# Take timepoints of interest for old trials, and collapse across hemispheres
roi_f = bind_rows("lh" = roi_lh, "rh" = roi_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, hemi, run, onset) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset) %>%
summarise(hipp_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character# Read in hipp tail BOLD
roi_tail_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-hippocampus-tail.csv')
roi_tail_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-hippocampus-tail.csv')
# Take timepoints of interest for old trials, and collapse across hemispheres
roi_tail_f = bind_rows("lh" = roi_tail_lh, "rh" = roi_tail_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, hemi, run, onset) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset) %>%
summarise(hipp_tail_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character# Read in angular BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-DefaultA_IPL.csv')
angular_lh_f = roi_lh %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, run, onset) %>%
summarise(angular_lh_sig = mean_(mean_activity)) %>% ungroup()
# Read in RSP BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-DefaultC_Rsp.csv')
roi_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-DefaultC_Rsp.csv')
# Take timepoints of interest for old trials, and collapse across hemispheres
rsp_f = bind_rows("lh" = roi_lh, "rh" = roi_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, hemi, run, onset) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset) %>%
summarise(rsp_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character# Read in CCN BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-frontoparietal.csv')
roi_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-frontoparietal.csv')
# dim(roi_lh); dim(roi_rh)
# Take timepoints of interest for old trials, and collapse across hemispheres
CCN_f = bind_rows("lh" = roi_lh, "rh" = roi_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, hemi, run, onset) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset) %>%
summarise(CCN_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character# also, just for LH
CCN_lh_f = roi_lh %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, run, onset) %>%
summarise(CCN_lh_sig = mean_(mean_activity)) %>% ungroup()
# Read in DAN BOLD
roi_lh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_lh-dorsalattn.csv')
roi_rh = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/extractraw_AP_mvpa_raw_rh-dorsalattn.csv')
# dim(roi_lh); dim(roi_rh)
# Take timepoints of interest for old trials, and collapse across hemispheres
DAN_f = bind_rows("lh" = roi_lh, "rh" = roi_rh, .id = "hemi") %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, hemi, run, onset) %>%
summarise(mean_activity = mean_(mean_activity)) %>%
group_by(subid, run, onset) %>%
summarise(DAN_sig = mean_(mean_activity)) %>% ungroup()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character# also, just for LH
DAN_lh_f = roi_lh %>%
filter(condition %in% c('sourcemiss_hi', 'sourcehit', 'itemhit_lo', 'M'),
time %in% c(4,6,8,10)) %>%
group_by(subid, run, onset) %>%
summarise(DAN_lh_sig = mean_(mean_activity)) %>% ungroup()
# Other reinstatement
d_infpar = read.csv('/Users/sgagnon/Dropbox/Stanford/Presentations/AP/mvpa_logit_inferiorparietal_place_byreps_avg_46810_filtartloc_scalewithinrun.csv') %>%
filter(!subid %in% subids_rm) %>%
filter(cond %in% c('sourcemiss_hi', 'sourcehit', 'M', 'itemhit_lo')) %>%
mutate(ang_logit = avg_logit) %>%
dplyr::select(subid, run, onset, ang_logit)
d_hipp = read.csv('/Users/sgagnon/Dropbox/Stanford/Presentations/AP/mvpa_logit_bilat-hippocampus_place_byreps_avg_46810_filtartloc_scalewithinrun.csv') %>%
filter(!subid %in% subids_rm) %>%
filter(cond %in% c('sourcemiss_hi', 'sourcehit', 'M', 'itemhit_lo')) %>%
mutate(pcorr = factor(ifelse(cond == "sourcehit", 1, 0)),
hipp_logit = avg_logit) %>%
dplyr::select(subid, run, onset, hipp_logit)
# Merge w/VTC classifier evidence
dm_ip = d %>%
left_join(roi_f, by=c('subid', 'run', 'onset')) %>%
left_join(roi_tail_f, by=c('subid', 'run', 'onset')) %>%
left_join(DAN_f, by=c('subid', 'run', 'onset')) %>%
left_join(CCN_f, by=c('subid', 'run', 'onset')) %>%
left_join(rsp_f, by=c('subid', 'run', 'onset')) %>%
left_join(angular_lh_f, by=c('subid', 'run', 'onset')) %>%
left_join(DAN_lh_f, by=c('subid', 'run', 'onset')) %>%
left_join(CCN_lh_f, by=c('subid', 'run', 'onset')) %>%
left_join(d_infpar, by=c('subid', 'run', 'onset')) %>%
left_join(d_hipp, by=c('subid', 'run', 'onset')) %>%
group_by(subid) %>%
mutate(ang_logit_z = zscore(ang_logit),
vtc_logit_z = zscore(vtc_logit),
hipp_logit_z = zscore(hipp_logit),
CCN_sig_z = zscore(CCN_sig),
rsp_sig_z = zscore(rsp_sig),
DAN_sig_z = zscore(DAN_sig),
angular_lh_sig_z = zscore(angular_lh_sig),
CCN_lh_sig_z = zscore(CCN_lh_sig),
DAN_lh_sig_z = zscore(DAN_lh_sig),
hipp_sig_z = zscore(hipp_sig),
hipp_tail_sig_z = zscore(hipp_tail_sig),
reps = factor(reps),
hipp_quintile = ntile(hipp_sig, 5),
rsp_quintile = ntile(rsp_sig, 5),
CCN_quintile = ntile(CCN_sig, 5),
DAN_quintile = ntile(DAN_sig, 5),
angular_lh_sig_quintile = ntile(angular_lh_sig, 5),
CCN_lh_quintile = ntile(CCN_lh_sig, 5),
DAN_lh_quintile = ntile(DAN_lh_sig, 5),
hipp_tail_quintile = ntile(hipp_tail_sig, 5),
vtc_quintile = ntile(vtc_logit, 5),
hipp_logit_quintile = ntile(hipp_logit, 5),
ang_quintile = ntile(ang_logit, 5)) %>%
ungroup() %>%
mutate(subid = factor(subid),
cond = factor(cond),
condition = factor(condition))## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vector## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vectordim(dm_ip) #just hipp: 6623 ## [1] 6780 82with(dm_ip, table(subid, reps))## reps
## subid 2 4
## ap100 76 78
## ap101 83 82
## ap102 80 79
## ap103 83 79
## ap104 82 83
## ap105 81 81
## ap107 79 82
## ap108 72 74
## ap109 81 77
## ap110 82 81
## ap111 82 84
## ap113 83 83
## ap114 81 81
## ap115 78 82
## ap116 80 82
## ap117 84 80
## ap118 83 81
## ap119 80 81
## ap120 79 76
## ap121 84 81
## ap122 84 82
## ap150 77 78
## ap152 73 78
## ap153 77 75
## ap154 67 78
## ap155 69 66
## ap157 77 79
## ap158 84 83
## ap159 81 81
## ap160 66 64
## ap161 81 79
## ap162 75 80
## ap163 83 78
## ap164 79 79
## ap165 75 81
## ap166 79 79
## ap167 76 77
## ap169 77 79
## ap170 81 81
## ap171 81 81
## ap172 80 76
## ap173 73 74
## ap174 77 80str(dm_ip)## Classes 'tbl_df', 'tbl' and 'data.frame': 6780 obs. of 82 variables:
## $ X.x : int 0 2 3 4 5 7 8 11 14 15 ...
## $ run : num 1 1 1 1 1 1 1 1 1 1 ...
## $ trial : int 1 3 4 5 6 8 9 12 15 16 ...
## $ onset : num 0.0191 19.8642 29.3132 40.6786 50.4701 ...
## $ duration : num 10.67 9.42 11.34 9.76 9.12 ...
## $ shockCond : Factor w/ 2 levels "safe","threat": 1 1 1 1 1 1 1 1 1 1 ...
## $ shockTrial : int 0 0 0 0 0 0 0 0 0 0 ...
## $ target : Factor w/ 252 levels "ACCORDIAN","ACROBAT",..: 15 238 116 114 183 224 87 102 192 191 ...
## $ associate : Factor w/ 241 levels "\002\002","\002",..: 68 237 126 61 175 233 53 118 173 84 ...
## $ resp : Factor w/ 4 levels "foil","indoor",..: 2 2 4 4 4 1 1 1 4 2 ...
## $ acc : Factor w/ 6 levels "CR","FA","H",..: 6 3 3 3 3 4 4 4 3 3 ...
## $ accSpec : Factor w/ 15 levels "CR","FAI_Hi",..: 14 6 8 8 8 10 10 10 8 6 ...
## $ respRT : num 2.11 1.68 1.73 1.37 2.15 ...
## $ subid : Factor w/ 43 levels "ap100","ap101",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ remove : logi NA NA NA NA NA NA ...
## $ anxious : int 3 3 3 3 3 3 3 3 3 3 ...
## $ happy : int 2 2 2 2 2 2 2 2 2 2 ...
## $ safe : int 3 3 3 3 3 3 3 3 3 3 ...
## $ stressed : int 3 3 3 3 3 3 3 3 3 3 ...
## $ life_stress : int 4 4 4 4 4 4 4 4 4 4 ...
## $ sleep : num 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 ...
## $ cond_orig : Factor w/ 5 levels "F_0","TI_2","TI_4",..: 5 2 4 4 5 3 2 4 5 2 ...
## $ shock_and_post : int 0 0 0 0 0 0 0 0 0 0 ...
## $ conf : Factor w/ 3 levels "Hi","Lo","N": 1 1 1 1 1 3 3 3 1 1 ...
## $ onset_adj : num 0.0191 19.8642 29.3132 40.6786 50.4701 ...
## $ mem_conditions : Factor w/ 7 levels "CR","FA","itemhit_lo",..: 7 6 6 6 6 4 4 4 6 6 ...
## $ obj_conditions : Factor w/ 3 levels "new","nuisance",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ onset_code : Factor w/ 6 levels "CR","FA","itemhit",..: 3 6 6 6 6 4 4 4 6 6 ...
## $ rt_z : num -1.2039 -0.746 -0.6739 -1.2235 -0.0194 ...
## $ mem_conditions_byrep : Factor w/ 13 levels "CR-0","FA-0",..: 13 10 10 10 11 6 5 5 11 10 ...
## $ onset_code_byrep : Factor w/ 11 levels "CR-0","FA-0",..: 4 10 10 10 11 6 5 5 11 10 ...
## $ img_type : Factor w/ 3 levels "foil","indoor",..: 3 2 3 3 3 2 2 3 3 2 ...
## $ X.y : int 2 8 11 14 17 23 26 35 44 47 ...
## $ index : int 2 8 11 14 17 23 26 35 44 47 ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## $ condition : Factor w/ 8 levels "itemhit_lo-2",..: 8 5 5 5 6 4 3 3 6 5 ...
## $ category : Factor w/ 1 level "place": 1 1 1 1 1 1 1 1 1 1 ...
## $ X0 : num -6.635 -9.265 6.804 8.431 0.203 ...
## $ X2 : num -6.32 -4.39 7.22 3.92 -3.54 ...
## $ X4 : num -1.49 5.79 4.22 1.8 -2.75 ...
## $ X6 : num 2.59 5.69 -3.03 3.7 3.74 ...
## $ X8 : num -1.03 8.65 -1.68 -1.28 -2.28 ...
## $ X10 : num -0.642 6.486 8.468 -0.23 -9.405 ...
## $ X12 : num -1.1 6.64 11.49 -3.55 -5.64 ...
## $ avg_logit : num -0.145 6.655 1.996 0.998 -2.673 ...
## $ cond : Factor w/ 4 levels "itemhit_lo","M",..: 4 3 3 3 3 2 2 2 3 3 ...
## $ reps : Factor w/ 2 levels "2","4": 2 1 1 1 2 2 1 1 2 1 ...
## $ pcorr : Factor w/ 2 levels "0","1": 1 2 2 2 2 1 1 1 2 2 ...
## $ vtc_logit : num -0.145 6.655 1.996 0.998 -2.673 ...
## $ imgType : Factor w/ 2 levels "indoor","outdoor": 2 1 2 2 2 1 1 2 2 1 ...
## $ hipp_sig : num 0.2754 0.4613 -0.1955 -0.0154 -0.14 ...
## $ hipp_tail_sig : num 0.11449 0.49252 -0.00556 -0.06895 -0.13196 ...
## $ DAN_sig : num 0.175 0.658 0.225 -0.262 -0.395 ...
## $ CCN_sig : num 0.367 0.4107 0.0192 -0.1273 -0.2068 ...
## $ rsp_sig : num 0.3053 1.5956 -0.0991 -0.19 -0.5123 ...
## $ angular_lh_sig : num 0.20058 0.85916 -0.09532 0.00574 -0.04293 ...
## $ DAN_lh_sig : num 0.212 0.585 0.183 -0.233 -0.383 ...
## $ CCN_lh_sig : num 0.3687 0.2566 0.1633 -0.1271 -0.0121 ...
## $ ang_logit : num -2.066 5.935 0.249 -3.792 -6.49 ...
## $ hipp_logit : num -2.553 1.418 -1.783 -2.571 -0.555 ...
## $ ang_logit_z : num -0.474 1.931 0.222 -0.993 -1.804 ...
## $ vtc_logit_z : num -0.148 1.895 0.495 0.195 -0.907 ...
## $ hipp_logit_z : num -0.491 1.406 -0.123 -0.499 0.464 ...
## $ CCN_sig_z : num 0.6436 0.7336 -0.0722 -0.3738 -0.5374 ...
## $ rsp_sig_z : num 0.209 2.071 -0.375 -0.506 -0.971 ...
## $ DAN_sig_z : num 0.391 1.606 0.516 -0.711 -1.046 ...
## $ angular_lh_sig_z : num 0.177 1.513 -0.423 -0.218 -0.317 ...
## $ CCN_lh_sig_z : num 0.537 0.31 0.121 -0.468 -0.235 ...
## $ DAN_lh_sig_z : num 0.437 1.389 0.363 -0.7 -1.082 ...
## $ hipp_sig_z : num 0.537 0.967 -0.552 -0.135 -0.424 ...
## $ hipp_tail_sig_z : num 0.167 1.146 -0.143 -0.307 -0.47 ...
## $ hipp_quintile : int 4 5 2 3 2 1 4 1 3 4 ...
## $ rsp_quintile : int 3 5 2 2 1 1 4 1 3 3 ...
## $ CCN_quintile : int 4 4 3 2 2 1 5 1 3 3 ...
## $ DAN_quintile : int 4 5 4 2 1 1 4 1 2 4 ...
## $ angular_lh_sig_quintile: int 3 5 2 3 2 1 2 1 4 4 ...
## $ CCN_lh_quintile : int 4 3 3 2 3 1 5 1 4 3 ...
## $ DAN_lh_quintile : int 4 5 4 2 1 1 4 1 3 4 ...
## $ hipp_tail_quintile : int 3 5 3 2 2 1 4 1 3 5 ...
## $ vtc_quintile : int 3 5 4 3 1 2 2 1 5 4 ...
## $ hipp_logit_quintile : int 2 5 3 2 4 2 5 3 1 3 ...
## $ ang_quintile : int 2 5 4 1 1 1 4 2 4 5 ...# set up contrasts
contrasts(dm_ip$pcorr) = c(-1,1); contrasts(dm_ip$pcorr)## [,1]
## 0 -1
## 1 1contrasts(dm_ip$reps) = c(-1,1); contrasts(dm_ip$reps)## [,1]
## 2 -1
## 4 1contrasts(dm_ip$group) = c(1,-1); contrasts(dm_ip$group)## [,1]
## control 1
## stress -1contrasts(dm_ip$shockCond) = c(1,-1); contrasts(dm_ip$shockCond)## [,1]
## safe 1
## threat -1Trial-wise analysis counts/group:
dm_ip %>% group_by(subid, group) %>% summarise(n()) %>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 22
## 2 stress 21HC assoc hits: infparietal reinstatement ~ group
subids_lowtrials = dm_ip %>%
filter(cond == 'sourcehit') %>%
group_by(group, subid) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), fill = list(count= 0)) %>%
filter(count < 6) %>%
pull(subid) %>% unique()
subids_lowtrials## factor(0)
## 43 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ... ap174d_avg = dm_ip %>% filter(!subid %in% subids_lowtrials) %>%
filter(cond == 'sourcehit') %>%
group_by(group, subid) %>%
summarise(mean=mean(ang_logit))
with(d_avg, table(group))## group
## control stress
## 22 21# diff between groups?
bartlett.test(mean ~ group, data=d_avg)##
## Bartlett test of homogeneity of variances
##
## data: mean by group
## Bartlett's K-squared = 1.2052, df = 1, p-value = 0.2723t.test(mean ~ group, data=d_avg, var.equal=TRUE)##
## Two Sample t-test
##
## data: mean by group
## t = 0.89075, df = 41, p-value = 0.3783
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2509866 0.6471018
## sample estimates:
## mean in group control mean in group stress
## 0.3410088 0.1429512d_avg %>% group_by(group) %>% summarise(mean(mean), n())## # A tibble: 2 x 3
## group `mean(mean)` `n()`
## <fctr> <dbl> <int>
## 1 control 0.3410088 22
## 2 stress 0.1429512 21t.test(d_avg %>% filter(group == 'control') %>% pull(mean), mu = 0)##
## One Sample t-test
##
## data: d_avg %>% filter(group == "control") %>% pull(mean)
## t = 2.5122, df = 21, p-value = 0.02024
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.05871977 0.62329777
## sample estimates:
## mean of x
## 0.3410088t.test(d_avg %>% filter(group == 'stress') %>% pull(mean), mu = 0)##
## One Sample t-test
##
## data: d_avg %>% filter(group == "stress") %>% pull(mean)
## t = 0.80435, df = 20, p-value = 0.4307
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.2277736 0.5136759
## sample estimates:
## mean of x
## 0.1429512HC accuracy mediation
dat = dtest %>%
filter(subj_status == 'old',
reps > 0, # old item
conf == 'Hi') %>%
group_by(subid, group) %>%
summarise(mean_acc = mean(acc_num), sum=sum(acc_num), count=n()) %>%
ungroup() %>%
complete(nesting(subid, group), fill = list(mean_acc=0,
count=0))
dat## # A tibble: 47 x 5
## subid group mean_acc sum count
## <fctr> <fctr> <dbl> <dbl> <dbl>
## 1 ap100 control 0.8725490 89 102
## 2 ap101 control 0.9375000 105 112
## 3 ap102 control 0.7010309 68 97
## 4 ap103 control 0.8968254 113 126
## 5 ap104 control 0.9130435 147 161
## 6 ap105 control 0.8623188 119 138
## 7 ap106 control 0.5087719 29 57
## 8 ap107 control 0.8802817 125 142
## 9 ap108 control 0.7073171 29 41
## 10 ap109 control 0.6489362 61 94
## # ... with 37 more rows# filter out subjs who don't have more than 5 "old" responses to old cues
dim(dat) # all subjs included## [1] 47 5dat %>% filter(count < 6) ## # A tibble: 1 x 5
## subid group mean_acc sum count
## <fctr> <fctr> <dbl> <dbl> <dbl>
## 1 ap151 stress 0.6 3 5rm_subids = dat %>% filter(count < 6) %>% pull(subid) %>% unique(); rm_subids## [1] ap151
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174rm_subids # ap151## [1] ap151
## 47 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap106 ap107 ap108 ... ap174dat = dat %>% filter(!subid %in% rm_subids)subids_lowtrials = dm_ip %>%
filter(cond == 'sourcehit') %>%
group_by(group, subid) %>%
summarise(count = n()) %>%
ungroup() %>%
complete(nesting(subid, group), fill = list(count= 0)) %>%
filter(count < 6) %>%
pull(subid) %>% unique()
subids_lowtrials## factor(0)
## 43 Levels: ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ... ap174with(dm_ip %>% filter(cond == 'sourcehit'), table(subid))## subid
## ap100 ap101 ap102 ap103 ap104 ap105 ap107 ap108 ap109 ap110 ap111 ap113
## 87 102 66 111 146 117 122 28 58 20 73 75
## ap114 ap115 ap116 ap117 ap118 ap119 ap120 ap121 ap122 ap150 ap152 ap153
## 101 77 144 84 62 112 71 33 95 66 40 31
## ap154 ap155 ap157 ap158 ap159 ap160 ap161 ap162 ap163 ap164 ap165 ap166
## 42 38 51 11 50 51 27 41 107 81 64 71
## ap167 ap169 ap170 ap171 ap172 ap173 ap174
## 38 69 74 33 20 8 84d_avg = dm_ip %>%
filter(cond == 'sourcehit') %>%
filter(!subid %in% subids_lowtrials) %>%
group_by(group, subid) %>%
summarise(mean=mean(ang_logit))
dim(d_avg)## [1] 43 3d_avg## Source: local data frame [43 x 3]
## Groups: group [?]
##
## # A tibble: 43 x 3
## group subid mean
## <fctr> <fctr> <dbl>
## 1 control ap100 0.2261302
## 2 control ap101 0.4469116
## 3 control ap102 0.8927461
## 4 control ap103 -0.7333308
## 5 control ap104 0.1633124
## 6 control ap105 0.3764084
## 7 control ap107 0.2016958
## 8 control ap108 -0.2507325
## 9 control ap109 -0.2083484
## 10 control ap110 0.3040365
## # ... with 33 more rowsvtc = d_avg %>% mutate(mean_logit = mean) %>% dplyr::select(-mean)
dim(vtc)## [1] 43 3vtc %>% group_by(subid, group) %>% summarise(n()) %>% group_by(group) %>% summarise(n())## # A tibble: 2 x 2
## group `n()`
## <fctr> <int>
## 1 control 22
## 2 stress 21roi = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/pe_sourcehit-cr_zstat1_peak1_5mm_sphere_masked.nii.csv')
roi = roi %>% filter(cond %in% c('sourcehit', 'CR')) %>%
mutate(cond = factor(cond)) %>%
dplyr::select(subid, group, cond, value) %>% # calc diff score
spread(key = cond, value=value) %>%
mutate(diff = sourcehit - CR) %>%
right_join(vtc, by = c('subid', 'group')) %>%
left_join(dat, by = c('subid', 'group')) %>%
mutate(diff_orig = diff,
diff = scale(diff),
mean_logit = scale(mean_logit),
mean_acc = as.numeric(scale(car::logit(mean_acc))))## Warning in right_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vector## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vectordim(roi)## [1] 43 10str(roi)## 'data.frame': 43 obs. of 10 variables:
## $ subid : chr "ap100" "ap101" "ap102" "ap103" ...
## $ group : Factor w/ 2 levels "control","stress": 1 1 1 1 1 1 1 1 1 1 ...
## $ CR : num -396 320 107 -519 -416 ...
## $ sourcehit : num 379.9 990.6 891.5 90.7 -99.2 ...
## $ diff : num [1:43, 1] 0.947 0.673 0.968 0.515 -0.246 ...
## ..- attr(*, "scaled:center")= num 411
## ..- attr(*, "scaled:scale")= num 385
## $ mean_logit: num [1:43, 1] -0.025 0.279 0.892 -1.345 -0.111 ...
## ..- attr(*, "scaled:center")= num 0.244
## ..- attr(*, "scaled:scale")= num 0.727
## $ mean_acc : num 0.44 1.43 -0.913 0.742 0.98 ...
## $ sum : num 89 105 68 113 147 119 125 29 61 20 ...
## $ count : num 102 112 97 126 161 138 142 41 94 21 ...
## $ diff_orig : num 776 671 784 610 317 ...contrasts(roi$group) = c(1,-1)
model.t = lm(mean_acc ~ diff, data=roi); summary(model.t) ##
## Call:
## lm(formula = mean_acc ~ diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.28067 -0.48665 0.01898 0.55006 1.87160
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.545e-17 1.339e-01 0.000 1.000000
## diff 4.971e-01 1.355e-01 3.668 0.000696 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8782 on 41 degrees of freedom
## Multiple R-squared: 0.2471, Adjusted R-squared: 0.2287
## F-statistic: 13.46 on 1 and 41 DF, p-value: 0.0006961model.m = lm(mean_logit ~ diff, data=roi); summary(model.m)##
## Call:
## lm(formula = mean_logit ~ diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5013 -0.6140 -0.1443 0.5246 2.2846
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.314e-17 1.470e-01 0.000 1.0000
## diff 3.041e-01 1.488e-01 2.044 0.0474 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9642 on 41 degrees of freedom
## Multiple R-squared: 0.09246, Adjusted R-squared: 0.07033
## F-statistic: 4.177 on 1 and 41 DF, p-value: 0.04743model.y = lm(mean_acc ~ mean_logit + diff, data=roi); summary(model.y)##
## Call:
## lm(formula = mean_acc ~ mean_logit + diff, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2776 -0.5048 0.0094 0.5413 1.8692
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.594e-17 1.356e-01 0.000 1.0000
## mean_logit 2.121e-02 1.440e-01 0.147 0.8836
## diff 4.907e-01 1.440e-01 3.408 0.0015 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8889 on 40 degrees of freedom
## Multiple R-squared: 0.2475, Adjusted R-squared: 0.2099
## F-statistic: 6.579 on 2 and 40 DF, p-value: 0.003388model.other = lm(mean_acc ~ mean_logit, data=roi); summary(model.other)##
## Call:
## lm(formula = mean_acc ~ mean_logit, data = roi)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1276 -0.8065 -0.1195 0.7939 2.0194
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.693e-17 1.521e-01 0.000 1.000
## mean_logit 1.704e-01 1.539e-01 1.107 0.275
##
## Residual standard error: 0.9973 on 41 degrees of freedom
## Multiple R-squared: 0.02904, Adjusted R-squared: 0.005354
## F-statistic: 1.226 on 1 and 41 DF, p-value: 0.2746## Bootstrapping Confidence Interval (BCa and Percentile)
boot.med <- function(data, indices){
data <-data[indices,] #this does the random resampling of rows
reg1 <- lm(mean_logit ~ diff, data=data)
reg2 <- lm(mean_acc ~ mean_logit + diff, data=data)
a <- coefficients(reg1)["diff"] #the a path coefficient
b <- coefficients(reg2)["mean_logit"] #the b path coefficient
a*b #returns the product of a*b
}
medboot<-boot(data=roi, statistic = boot.med, R=5000)
mean(medboot$t)## [1] 0.002536981quantile(medboot$t, c(0.025, 0.975))## 2.5% 97.5%
## -0.10593348 0.09848875# [1] 0.001264906
# 2.5% 97.5%
# -0.11376327 0.09313285Other analyses
Is PPA activity reduced for item hits relative to source hits?
# dat = read.csv('/Volumes/group/awagner/sgagnon/AP/analysis/ap_memory_raw/group/roi/pe_ppa_scene_limitvox.csv')
# dat = dat %>% filter(cond %in% c('sourcehit',
# 'itemhit_lo')) %>%
# mutate(cond = factor(cond))
# str(dat)
#
# subids_lowtrials = dm %>%
# filter(cond %in% c('itemhit_lo', 'sourcehit')) %>%
# mutate(cond = factor(cond)) %>%
# group_by(group, cond, subid) %>%
# summarise(count = n()) %>%
# ungroup() %>%
# complete(nesting(subid, group), cond,
# fill = list(count= 0)) %>%
# filter(count < 6) %>%
# pull(subid) %>% unique()
# print(subids_lowtrials)
#
# dat = dat %>% filter(!subid %in% subids_lowtrials) %>%
# mutate(subid = factor(subid))
# with(dat, table(subid))
# head(dat)
#
# contrasts(dat$group) = c(1,-1); contrasts(dat$group)
# contrasts(dat$cond) = c(-1,1); contrasts(dat$cond)
# summary(lmer(value ~ group * cond + (1|subid), data=dat)) # rand slope, not enough obs
#
# bartlett.test(value ~ group, data=dat %>% filter(cond == 'sourcehit'))
# t.test(value ~ group, data=dat %>% filter(cond == 'sourcehit'), var.equal=TRUE)
# t.test(value ~ group, data=dat %>% filter(cond == 'itemhit_lo'), var.equal=TRUE)
#
# dat %>% filter(cond == 'itemhit_lo') %>% pull(value)
# t.test(dat %>% filter(cond == 'itemhit_lo') %>% pull(value), mu=0)
#
# dat %>% group_by(group, cond) %>% summarise(mean = mean(value),
# se = std.error(value), n())